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Conformal Predictions for Probabilistically Robust Scalable Machine Learning Classification (2403.10368v1)
Published 15 Mar 2024 in stat.ML, cs.CR, and cs.LG
Abstract: Conformal predictions make it possible to define reliable and robust learning algorithms. But they are essentially a method for evaluating whether an algorithm is good enough to be used in practice. To define a reliable learning framework for classification from the very beginning of its design, the concept of scalable classifier was introduced to generalize the concept of classical classifier by linking it to statistical order theory and probabilistic learning theory. In this paper, we analyze the similarities between scalable classifiers and conformal predictions by introducing a new definition of a score function and defining a special set of input variables, the conformal safety set, which can identify patterns in the input space that satisfy the error coverage guarantee, i.e., that the probability of observing the wrong (possibly unsafe) label for points belonging to this set is bounded by a predefined $\varepsilon$ error level. We demonstrate the practical implications of this framework through an application in cybersecurity for identifying DNS tunneling attacks. Our work contributes to the development of probabilistically robust and reliable machine learning models.
- Vovk, V., Gammerman, A., Shafer, G.: Algorithmic Learning in a Random World. Springer, Berlin, Heidelberg (2005) Vovk et al. [2017] Vovk, V., Shen, J., Manokhin, V., Xie, M.: Nonparametric predictive distributions based on conformal prediction. In: Conformal and Probabilistic Prediction and Applications, pp. 82–102 (2017) Vovk et al. [2022] Vovk, V., Gammerman, A., Shafer, G.: Probabilistic Classification: Venn Predictors, pp. 157–179. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-06649-8_6 Angelopoulos and Bates [2023] Angelopoulos, A.N., Bates, S.: Conformal prediction: A gentle introduction. Foundations and Trends® in Machine Learning 16(4), 494–591 (2023) https://doi.org/10.1561/2200000101 Fontana et al. [2023] Fontana, M., Zeni, G., Vantini, S.: Conformal prediction: A unified review of theory and new challenges. Bernoulli 29(1), 1–23 (2023) https://doi.org/10.3150/21-BEJ1447 Toccaceli [2022] Toccaceli, P.: Introduction to conformal predictors. Pattern Recognition 124, 108507 (2022) https://doi.org/10.1016/j.patcog.2021.108507 Forreryd et al. [2018] Forreryd, A., Norinder, U., Lindberg, T., Lindstedt, M.: Predicting skin sensitizers with confidence — using conformal prediction to determine applicability domain of gard. Toxicology in Vitro 48, 179–187 (2018) https://doi.org/10.1016/j.tiv.2018.01.021 Balasubramanian et al. [2009] Balasubramanian, V.N., Gouripeddi, R., Panchanathan, S., Vermillion, J., Bhaskaran, A., Siegel, R.: Support vector machine based conformal predictors for risk of complications following a coronary drug eluting stent procedure. In: 2009 36th Annual Computers in Cardiology Conference (CinC), pp. 5–8 (2009). IEEE Narteni et al. [2023] Narteni, S., Carlevaro, A., Dabbene, F., Muselli, M., Mongelli, M.: Confiderai: Conformal interpretable-by-design score function for explainable and reliable artificial intelligence. In: Conformal and Probabilistic Prediction with Applications, pp. 485–487 (2023) Angelopoulos et al. [2020] Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vovk, V., Shen, J., Manokhin, V., Xie, M.: Nonparametric predictive distributions based on conformal prediction. In: Conformal and Probabilistic Prediction and Applications, pp. 82–102 (2017) Vovk et al. [2022] Vovk, V., Gammerman, A., Shafer, G.: Probabilistic Classification: Venn Predictors, pp. 157–179. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-06649-8_6 Angelopoulos and Bates [2023] Angelopoulos, A.N., Bates, S.: Conformal prediction: A gentle introduction. Foundations and Trends® in Machine Learning 16(4), 494–591 (2023) https://doi.org/10.1561/2200000101 Fontana et al. [2023] Fontana, M., Zeni, G., Vantini, S.: Conformal prediction: A unified review of theory and new challenges. Bernoulli 29(1), 1–23 (2023) https://doi.org/10.3150/21-BEJ1447 Toccaceli [2022] Toccaceli, P.: Introduction to conformal predictors. Pattern Recognition 124, 108507 (2022) https://doi.org/10.1016/j.patcog.2021.108507 Forreryd et al. [2018] Forreryd, A., Norinder, U., Lindberg, T., Lindstedt, M.: Predicting skin sensitizers with confidence — using conformal prediction to determine applicability domain of gard. Toxicology in Vitro 48, 179–187 (2018) https://doi.org/10.1016/j.tiv.2018.01.021 Balasubramanian et al. [2009] Balasubramanian, V.N., Gouripeddi, R., Panchanathan, S., Vermillion, J., Bhaskaran, A., Siegel, R.: Support vector machine based conformal predictors for risk of complications following a coronary drug eluting stent procedure. In: 2009 36th Annual Computers in Cardiology Conference (CinC), pp. 5–8 (2009). IEEE Narteni et al. [2023] Narteni, S., Carlevaro, A., Dabbene, F., Muselli, M., Mongelli, M.: Confiderai: Conformal interpretable-by-design score function for explainable and reliable artificial intelligence. In: Conformal and Probabilistic Prediction with Applications, pp. 485–487 (2023) Angelopoulos et al. [2020] Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vovk, V., Gammerman, A., Shafer, G.: Probabilistic Classification: Venn Predictors, pp. 157–179. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-06649-8_6 Angelopoulos and Bates [2023] Angelopoulos, A.N., Bates, S.: Conformal prediction: A gentle introduction. Foundations and Trends® in Machine Learning 16(4), 494–591 (2023) https://doi.org/10.1561/2200000101 Fontana et al. [2023] Fontana, M., Zeni, G., Vantini, S.: Conformal prediction: A unified review of theory and new challenges. Bernoulli 29(1), 1–23 (2023) https://doi.org/10.3150/21-BEJ1447 Toccaceli [2022] Toccaceli, P.: Introduction to conformal predictors. Pattern Recognition 124, 108507 (2022) https://doi.org/10.1016/j.patcog.2021.108507 Forreryd et al. [2018] Forreryd, A., Norinder, U., Lindberg, T., Lindstedt, M.: Predicting skin sensitizers with confidence — using conformal prediction to determine applicability domain of gard. Toxicology in Vitro 48, 179–187 (2018) https://doi.org/10.1016/j.tiv.2018.01.021 Balasubramanian et al. [2009] Balasubramanian, V.N., Gouripeddi, R., Panchanathan, S., Vermillion, J., Bhaskaran, A., Siegel, R.: Support vector machine based conformal predictors for risk of complications following a coronary drug eluting stent procedure. In: 2009 36th Annual Computers in Cardiology Conference (CinC), pp. 5–8 (2009). IEEE Narteni et al. [2023] Narteni, S., Carlevaro, A., Dabbene, F., Muselli, M., Mongelli, M.: Confiderai: Conformal interpretable-by-design score function for explainable and reliable artificial intelligence. In: Conformal and Probabilistic Prediction with Applications, pp. 485–487 (2023) Angelopoulos et al. [2020] Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Angelopoulos, A.N., Bates, S.: Conformal prediction: A gentle introduction. Foundations and Trends® in Machine Learning 16(4), 494–591 (2023) https://doi.org/10.1561/2200000101 Fontana et al. [2023] Fontana, M., Zeni, G., Vantini, S.: Conformal prediction: A unified review of theory and new challenges. Bernoulli 29(1), 1–23 (2023) https://doi.org/10.3150/21-BEJ1447 Toccaceli [2022] Toccaceli, P.: Introduction to conformal predictors. Pattern Recognition 124, 108507 (2022) https://doi.org/10.1016/j.patcog.2021.108507 Forreryd et al. [2018] Forreryd, A., Norinder, U., Lindberg, T., Lindstedt, M.: Predicting skin sensitizers with confidence — using conformal prediction to determine applicability domain of gard. Toxicology in Vitro 48, 179–187 (2018) https://doi.org/10.1016/j.tiv.2018.01.021 Balasubramanian et al. [2009] Balasubramanian, V.N., Gouripeddi, R., Panchanathan, S., Vermillion, J., Bhaskaran, A., Siegel, R.: Support vector machine based conformal predictors for risk of complications following a coronary drug eluting stent procedure. In: 2009 36th Annual Computers in Cardiology Conference (CinC), pp. 5–8 (2009). IEEE Narteni et al. [2023] Narteni, S., Carlevaro, A., Dabbene, F., Muselli, M., Mongelli, M.: Confiderai: Conformal interpretable-by-design score function for explainable and reliable artificial intelligence. In: Conformal and Probabilistic Prediction with Applications, pp. 485–487 (2023) Angelopoulos et al. [2020] Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Fontana, M., Zeni, G., Vantini, S.: Conformal prediction: A unified review of theory and new challenges. Bernoulli 29(1), 1–23 (2023) https://doi.org/10.3150/21-BEJ1447 Toccaceli [2022] Toccaceli, P.: Introduction to conformal predictors. Pattern Recognition 124, 108507 (2022) https://doi.org/10.1016/j.patcog.2021.108507 Forreryd et al. [2018] Forreryd, A., Norinder, U., Lindberg, T., Lindstedt, M.: Predicting skin sensitizers with confidence — using conformal prediction to determine applicability domain of gard. Toxicology in Vitro 48, 179–187 (2018) https://doi.org/10.1016/j.tiv.2018.01.021 Balasubramanian et al. [2009] Balasubramanian, V.N., Gouripeddi, R., Panchanathan, S., Vermillion, J., Bhaskaran, A., Siegel, R.: Support vector machine based conformal predictors for risk of complications following a coronary drug eluting stent procedure. In: 2009 36th Annual Computers in Cardiology Conference (CinC), pp. 5–8 (2009). IEEE Narteni et al. [2023] Narteni, S., Carlevaro, A., Dabbene, F., Muselli, M., Mongelli, M.: Confiderai: Conformal interpretable-by-design score function for explainable and reliable artificial intelligence. In: Conformal and Probabilistic Prediction with Applications, pp. 485–487 (2023) Angelopoulos et al. [2020] Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Toccaceli, P.: Introduction to conformal predictors. Pattern Recognition 124, 108507 (2022) https://doi.org/10.1016/j.patcog.2021.108507 Forreryd et al. [2018] Forreryd, A., Norinder, U., Lindberg, T., Lindstedt, M.: Predicting skin sensitizers with confidence — using conformal prediction to determine applicability domain of gard. Toxicology in Vitro 48, 179–187 (2018) https://doi.org/10.1016/j.tiv.2018.01.021 Balasubramanian et al. [2009] Balasubramanian, V.N., Gouripeddi, R., Panchanathan, S., Vermillion, J., Bhaskaran, A., Siegel, R.: Support vector machine based conformal predictors for risk of complications following a coronary drug eluting stent procedure. In: 2009 36th Annual Computers in Cardiology Conference (CinC), pp. 5–8 (2009). IEEE Narteni et al. [2023] Narteni, S., Carlevaro, A., Dabbene, F., Muselli, M., Mongelli, M.: Confiderai: Conformal interpretable-by-design score function for explainable and reliable artificial intelligence. In: Conformal and Probabilistic Prediction with Applications, pp. 485–487 (2023) Angelopoulos et al. [2020] Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Forreryd, A., Norinder, U., Lindberg, T., Lindstedt, M.: Predicting skin sensitizers with confidence — using conformal prediction to determine applicability domain of gard. Toxicology in Vitro 48, 179–187 (2018) https://doi.org/10.1016/j.tiv.2018.01.021 Balasubramanian et al. [2009] Balasubramanian, V.N., Gouripeddi, R., Panchanathan, S., Vermillion, J., Bhaskaran, A., Siegel, R.: Support vector machine based conformal predictors for risk of complications following a coronary drug eluting stent procedure. In: 2009 36th Annual Computers in Cardiology Conference (CinC), pp. 5–8 (2009). IEEE Narteni et al. [2023] Narteni, S., Carlevaro, A., Dabbene, F., Muselli, M., Mongelli, M.: Confiderai: Conformal interpretable-by-design score function for explainable and reliable artificial intelligence. In: Conformal and Probabilistic Prediction with Applications, pp. 485–487 (2023) Angelopoulos et al. [2020] Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Balasubramanian, V.N., Gouripeddi, R., Panchanathan, S., Vermillion, J., Bhaskaran, A., Siegel, R.: Support vector machine based conformal predictors for risk of complications following a coronary drug eluting stent procedure. In: 2009 36th Annual Computers in Cardiology Conference (CinC), pp. 5–8 (2009). IEEE Narteni et al. [2023] Narteni, S., Carlevaro, A., Dabbene, F., Muselli, M., Mongelli, M.: Confiderai: Conformal interpretable-by-design score function for explainable and reliable artificial intelligence. In: Conformal and Probabilistic Prediction with Applications, pp. 485–487 (2023) Angelopoulos et al. [2020] Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Narteni, S., Carlevaro, A., Dabbene, F., Muselli, M., Mongelli, M.: Confiderai: Conformal interpretable-by-design score function for explainable and reliable artificial intelligence. In: Conformal and Probabilistic Prediction with Applications, pp. 485–487 (2023) Angelopoulos et al. [2020] Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299
- Vovk, V., Shen, J., Manokhin, V., Xie, M.: Nonparametric predictive distributions based on conformal prediction. In: Conformal and Probabilistic Prediction and Applications, pp. 82–102 (2017) Vovk et al. [2022] Vovk, V., Gammerman, A., Shafer, G.: Probabilistic Classification: Venn Predictors, pp. 157–179. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-06649-8_6 Angelopoulos and Bates [2023] Angelopoulos, A.N., Bates, S.: Conformal prediction: A gentle introduction. Foundations and Trends® in Machine Learning 16(4), 494–591 (2023) https://doi.org/10.1561/2200000101 Fontana et al. [2023] Fontana, M., Zeni, G., Vantini, S.: Conformal prediction: A unified review of theory and new challenges. Bernoulli 29(1), 1–23 (2023) https://doi.org/10.3150/21-BEJ1447 Toccaceli [2022] Toccaceli, P.: Introduction to conformal predictors. Pattern Recognition 124, 108507 (2022) https://doi.org/10.1016/j.patcog.2021.108507 Forreryd et al. [2018] Forreryd, A., Norinder, U., Lindberg, T., Lindstedt, M.: Predicting skin sensitizers with confidence — using conformal prediction to determine applicability domain of gard. Toxicology in Vitro 48, 179–187 (2018) https://doi.org/10.1016/j.tiv.2018.01.021 Balasubramanian et al. [2009] Balasubramanian, V.N., Gouripeddi, R., Panchanathan, S., Vermillion, J., Bhaskaran, A., Siegel, R.: Support vector machine based conformal predictors for risk of complications following a coronary drug eluting stent procedure. In: 2009 36th Annual Computers in Cardiology Conference (CinC), pp. 5–8 (2009). IEEE Narteni et al. [2023] Narteni, S., Carlevaro, A., Dabbene, F., Muselli, M., Mongelli, M.: Confiderai: Conformal interpretable-by-design score function for explainable and reliable artificial intelligence. In: Conformal and Probabilistic Prediction with Applications, pp. 485–487 (2023) Angelopoulos et al. [2020] Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vovk, V., Gammerman, A., Shafer, G.: Probabilistic Classification: Venn Predictors, pp. 157–179. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-06649-8_6 Angelopoulos and Bates [2023] Angelopoulos, A.N., Bates, S.: Conformal prediction: A gentle introduction. Foundations and Trends® in Machine Learning 16(4), 494–591 (2023) https://doi.org/10.1561/2200000101 Fontana et al. [2023] Fontana, M., Zeni, G., Vantini, S.: Conformal prediction: A unified review of theory and new challenges. Bernoulli 29(1), 1–23 (2023) https://doi.org/10.3150/21-BEJ1447 Toccaceli [2022] Toccaceli, P.: Introduction to conformal predictors. Pattern Recognition 124, 108507 (2022) https://doi.org/10.1016/j.patcog.2021.108507 Forreryd et al. [2018] Forreryd, A., Norinder, U., Lindberg, T., Lindstedt, M.: Predicting skin sensitizers with confidence — using conformal prediction to determine applicability domain of gard. Toxicology in Vitro 48, 179–187 (2018) https://doi.org/10.1016/j.tiv.2018.01.021 Balasubramanian et al. [2009] Balasubramanian, V.N., Gouripeddi, R., Panchanathan, S., Vermillion, J., Bhaskaran, A., Siegel, R.: Support vector machine based conformal predictors for risk of complications following a coronary drug eluting stent procedure. In: 2009 36th Annual Computers in Cardiology Conference (CinC), pp. 5–8 (2009). IEEE Narteni et al. [2023] Narteni, S., Carlevaro, A., Dabbene, F., Muselli, M., Mongelli, M.: Confiderai: Conformal interpretable-by-design score function for explainable and reliable artificial intelligence. In: Conformal and Probabilistic Prediction with Applications, pp. 485–487 (2023) Angelopoulos et al. [2020] Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Angelopoulos, A.N., Bates, S.: Conformal prediction: A gentle introduction. Foundations and Trends® in Machine Learning 16(4), 494–591 (2023) https://doi.org/10.1561/2200000101 Fontana et al. [2023] Fontana, M., Zeni, G., Vantini, S.: Conformal prediction: A unified review of theory and new challenges. Bernoulli 29(1), 1–23 (2023) https://doi.org/10.3150/21-BEJ1447 Toccaceli [2022] Toccaceli, P.: Introduction to conformal predictors. Pattern Recognition 124, 108507 (2022) https://doi.org/10.1016/j.patcog.2021.108507 Forreryd et al. [2018] Forreryd, A., Norinder, U., Lindberg, T., Lindstedt, M.: Predicting skin sensitizers with confidence — using conformal prediction to determine applicability domain of gard. Toxicology in Vitro 48, 179–187 (2018) https://doi.org/10.1016/j.tiv.2018.01.021 Balasubramanian et al. [2009] Balasubramanian, V.N., Gouripeddi, R., Panchanathan, S., Vermillion, J., Bhaskaran, A., Siegel, R.: Support vector machine based conformal predictors for risk of complications following a coronary drug eluting stent procedure. In: 2009 36th Annual Computers in Cardiology Conference (CinC), pp. 5–8 (2009). IEEE Narteni et al. [2023] Narteni, S., Carlevaro, A., Dabbene, F., Muselli, M., Mongelli, M.: Confiderai: Conformal interpretable-by-design score function for explainable and reliable artificial intelligence. In: Conformal and Probabilistic Prediction with Applications, pp. 485–487 (2023) Angelopoulos et al. [2020] Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Fontana, M., Zeni, G., Vantini, S.: Conformal prediction: A unified review of theory and new challenges. Bernoulli 29(1), 1–23 (2023) https://doi.org/10.3150/21-BEJ1447 Toccaceli [2022] Toccaceli, P.: Introduction to conformal predictors. Pattern Recognition 124, 108507 (2022) https://doi.org/10.1016/j.patcog.2021.108507 Forreryd et al. [2018] Forreryd, A., Norinder, U., Lindberg, T., Lindstedt, M.: Predicting skin sensitizers with confidence — using conformal prediction to determine applicability domain of gard. Toxicology in Vitro 48, 179–187 (2018) https://doi.org/10.1016/j.tiv.2018.01.021 Balasubramanian et al. [2009] Balasubramanian, V.N., Gouripeddi, R., Panchanathan, S., Vermillion, J., Bhaskaran, A., Siegel, R.: Support vector machine based conformal predictors for risk of complications following a coronary drug eluting stent procedure. In: 2009 36th Annual Computers in Cardiology Conference (CinC), pp. 5–8 (2009). IEEE Narteni et al. [2023] Narteni, S., Carlevaro, A., Dabbene, F., Muselli, M., Mongelli, M.: Confiderai: Conformal interpretable-by-design score function for explainable and reliable artificial intelligence. In: Conformal and Probabilistic Prediction with Applications, pp. 485–487 (2023) Angelopoulos et al. [2020] Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Toccaceli, P.: Introduction to conformal predictors. Pattern Recognition 124, 108507 (2022) https://doi.org/10.1016/j.patcog.2021.108507 Forreryd et al. [2018] Forreryd, A., Norinder, U., Lindberg, T., Lindstedt, M.: Predicting skin sensitizers with confidence — using conformal prediction to determine applicability domain of gard. Toxicology in Vitro 48, 179–187 (2018) https://doi.org/10.1016/j.tiv.2018.01.021 Balasubramanian et al. [2009] Balasubramanian, V.N., Gouripeddi, R., Panchanathan, S., Vermillion, J., Bhaskaran, A., Siegel, R.: Support vector machine based conformal predictors for risk of complications following a coronary drug eluting stent procedure. In: 2009 36th Annual Computers in Cardiology Conference (CinC), pp. 5–8 (2009). IEEE Narteni et al. [2023] Narteni, S., Carlevaro, A., Dabbene, F., Muselli, M., Mongelli, M.: Confiderai: Conformal interpretable-by-design score function for explainable and reliable artificial intelligence. In: Conformal and Probabilistic Prediction with Applications, pp. 485–487 (2023) Angelopoulos et al. [2020] Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Forreryd, A., Norinder, U., Lindberg, T., Lindstedt, M.: Predicting skin sensitizers with confidence — using conformal prediction to determine applicability domain of gard. Toxicology in Vitro 48, 179–187 (2018) https://doi.org/10.1016/j.tiv.2018.01.021 Balasubramanian et al. [2009] Balasubramanian, V.N., Gouripeddi, R., Panchanathan, S., Vermillion, J., Bhaskaran, A., Siegel, R.: Support vector machine based conformal predictors for risk of complications following a coronary drug eluting stent procedure. In: 2009 36th Annual Computers in Cardiology Conference (CinC), pp. 5–8 (2009). IEEE Narteni et al. [2023] Narteni, S., Carlevaro, A., Dabbene, F., Muselli, M., Mongelli, M.: Confiderai: Conformal interpretable-by-design score function for explainable and reliable artificial intelligence. In: Conformal and Probabilistic Prediction with Applications, pp. 485–487 (2023) Angelopoulos et al. [2020] Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Balasubramanian, V.N., Gouripeddi, R., Panchanathan, S., Vermillion, J., Bhaskaran, A., Siegel, R.: Support vector machine based conformal predictors for risk of complications following a coronary drug eluting stent procedure. In: 2009 36th Annual Computers in Cardiology Conference (CinC), pp. 5–8 (2009). IEEE Narteni et al. [2023] Narteni, S., Carlevaro, A., Dabbene, F., Muselli, M., Mongelli, M.: Confiderai: Conformal interpretable-by-design score function for explainable and reliable artificial intelligence. In: Conformal and Probabilistic Prediction with Applications, pp. 485–487 (2023) Angelopoulos et al. [2020] Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Narteni, S., Carlevaro, A., Dabbene, F., Muselli, M., Mongelli, M.: Confiderai: Conformal interpretable-by-design score function for explainable and reliable artificial intelligence. In: Conformal and Probabilistic Prediction with Applications, pp. 485–487 (2023) Angelopoulos et al. [2020] Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299
- Vovk, V., Gammerman, A., Shafer, G.: Probabilistic Classification: Venn Predictors, pp. 157–179. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-06649-8_6 Angelopoulos and Bates [2023] Angelopoulos, A.N., Bates, S.: Conformal prediction: A gentle introduction. Foundations and Trends® in Machine Learning 16(4), 494–591 (2023) https://doi.org/10.1561/2200000101 Fontana et al. [2023] Fontana, M., Zeni, G., Vantini, S.: Conformal prediction: A unified review of theory and new challenges. Bernoulli 29(1), 1–23 (2023) https://doi.org/10.3150/21-BEJ1447 Toccaceli [2022] Toccaceli, P.: Introduction to conformal predictors. Pattern Recognition 124, 108507 (2022) https://doi.org/10.1016/j.patcog.2021.108507 Forreryd et al. [2018] Forreryd, A., Norinder, U., Lindberg, T., Lindstedt, M.: Predicting skin sensitizers with confidence — using conformal prediction to determine applicability domain of gard. 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(2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Angelopoulos, A.N., Bates, S.: Conformal prediction: A gentle introduction. Foundations and Trends® in Machine Learning 16(4), 494–591 (2023) https://doi.org/10.1561/2200000101 Fontana et al. [2023] Fontana, M., Zeni, G., Vantini, S.: Conformal prediction: A unified review of theory and new challenges. Bernoulli 29(1), 1–23 (2023) https://doi.org/10.3150/21-BEJ1447 Toccaceli [2022] Toccaceli, P.: Introduction to conformal predictors. Pattern Recognition 124, 108507 (2022) https://doi.org/10.1016/j.patcog.2021.108507 Forreryd et al. [2018] Forreryd, A., Norinder, U., Lindberg, T., Lindstedt, M.: Predicting skin sensitizers with confidence — using conformal prediction to determine applicability domain of gard. Toxicology in Vitro 48, 179–187 (2018) https://doi.org/10.1016/j.tiv.2018.01.021 Balasubramanian et al. [2009] Balasubramanian, V.N., Gouripeddi, R., Panchanathan, S., Vermillion, J., Bhaskaran, A., Siegel, R.: Support vector machine based conformal predictors for risk of complications following a coronary drug eluting stent procedure. In: 2009 36th Annual Computers in Cardiology Conference (CinC), pp. 5–8 (2009). IEEE Narteni et al. [2023] Narteni, S., Carlevaro, A., Dabbene, F., Muselli, M., Mongelli, M.: Confiderai: Conformal interpretable-by-design score function for explainable and reliable artificial intelligence. In: Conformal and Probabilistic Prediction with Applications, pp. 485–487 (2023) Angelopoulos et al. [2020] Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Fontana, M., Zeni, G., Vantini, S.: Conformal prediction: A unified review of theory and new challenges. Bernoulli 29(1), 1–23 (2023) https://doi.org/10.3150/21-BEJ1447 Toccaceli [2022] Toccaceli, P.: Introduction to conformal predictors. Pattern Recognition 124, 108507 (2022) https://doi.org/10.1016/j.patcog.2021.108507 Forreryd et al. [2018] Forreryd, A., Norinder, U., Lindberg, T., Lindstedt, M.: Predicting skin sensitizers with confidence — using conformal prediction to determine applicability domain of gard. Toxicology in Vitro 48, 179–187 (2018) https://doi.org/10.1016/j.tiv.2018.01.021 Balasubramanian et al. [2009] Balasubramanian, V.N., Gouripeddi, R., Panchanathan, S., Vermillion, J., Bhaskaran, A., Siegel, R.: Support vector machine based conformal predictors for risk of complications following a coronary drug eluting stent procedure. In: 2009 36th Annual Computers in Cardiology Conference (CinC), pp. 5–8 (2009). IEEE Narteni et al. [2023] Narteni, S., Carlevaro, A., Dabbene, F., Muselli, M., Mongelli, M.: Confiderai: Conformal interpretable-by-design score function for explainable and reliable artificial intelligence. In: Conformal and Probabilistic Prediction with Applications, pp. 485–487 (2023) Angelopoulos et al. [2020] Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Toccaceli, P.: Introduction to conformal predictors. Pattern Recognition 124, 108507 (2022) https://doi.org/10.1016/j.patcog.2021.108507 Forreryd et al. [2018] Forreryd, A., Norinder, U., Lindberg, T., Lindstedt, M.: Predicting skin sensitizers with confidence — using conformal prediction to determine applicability domain of gard. Toxicology in Vitro 48, 179–187 (2018) https://doi.org/10.1016/j.tiv.2018.01.021 Balasubramanian et al. [2009] Balasubramanian, V.N., Gouripeddi, R., Panchanathan, S., Vermillion, J., Bhaskaran, A., Siegel, R.: Support vector machine based conformal predictors for risk of complications following a coronary drug eluting stent procedure. In: 2009 36th Annual Computers in Cardiology Conference (CinC), pp. 5–8 (2009). IEEE Narteni et al. [2023] Narteni, S., Carlevaro, A., Dabbene, F., Muselli, M., Mongelli, M.: Confiderai: Conformal interpretable-by-design score function for explainable and reliable artificial intelligence. In: Conformal and Probabilistic Prediction with Applications, pp. 485–487 (2023) Angelopoulos et al. [2020] Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Forreryd, A., Norinder, U., Lindberg, T., Lindstedt, M.: Predicting skin sensitizers with confidence — using conformal prediction to determine applicability domain of gard. Toxicology in Vitro 48, 179–187 (2018) https://doi.org/10.1016/j.tiv.2018.01.021 Balasubramanian et al. [2009] Balasubramanian, V.N., Gouripeddi, R., Panchanathan, S., Vermillion, J., Bhaskaran, A., Siegel, R.: Support vector machine based conformal predictors for risk of complications following a coronary drug eluting stent procedure. In: 2009 36th Annual Computers in Cardiology Conference (CinC), pp. 5–8 (2009). IEEE Narteni et al. [2023] Narteni, S., Carlevaro, A., Dabbene, F., Muselli, M., Mongelli, M.: Confiderai: Conformal interpretable-by-design score function for explainable and reliable artificial intelligence. In: Conformal and Probabilistic Prediction with Applications, pp. 485–487 (2023) Angelopoulos et al. [2020] Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Balasubramanian, V.N., Gouripeddi, R., Panchanathan, S., Vermillion, J., Bhaskaran, A., Siegel, R.: Support vector machine based conformal predictors for risk of complications following a coronary drug eluting stent procedure. In: 2009 36th Annual Computers in Cardiology Conference (CinC), pp. 5–8 (2009). IEEE Narteni et al. [2023] Narteni, S., Carlevaro, A., Dabbene, F., Muselli, M., Mongelli, M.: Confiderai: Conformal interpretable-by-design score function for explainable and reliable artificial intelligence. In: Conformal and Probabilistic Prediction with Applications, pp. 485–487 (2023) Angelopoulos et al. [2020] Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Narteni, S., Carlevaro, A., Dabbene, F., Muselli, M., Mongelli, M.: Confiderai: Conformal interpretable-by-design score function for explainable and reliable artificial intelligence. In: Conformal and Probabilistic Prediction with Applications, pp. 485–487 (2023) Angelopoulos et al. [2020] Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. 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(2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299
- Angelopoulos, A.N., Bates, S.: Conformal prediction: A gentle introduction. Foundations and Trends® in Machine Learning 16(4), 494–591 (2023) https://doi.org/10.1561/2200000101 Fontana et al. [2023] Fontana, M., Zeni, G., Vantini, S.: Conformal prediction: A unified review of theory and new challenges. Bernoulli 29(1), 1–23 (2023) https://doi.org/10.3150/21-BEJ1447 Toccaceli [2022] Toccaceli, P.: Introduction to conformal predictors. Pattern Recognition 124, 108507 (2022) https://doi.org/10.1016/j.patcog.2021.108507 Forreryd et al. [2018] Forreryd, A., Norinder, U., Lindberg, T., Lindstedt, M.: Predicting skin sensitizers with confidence — using conformal prediction to determine applicability domain of gard. Toxicology in Vitro 48, 179–187 (2018) https://doi.org/10.1016/j.tiv.2018.01.021 Balasubramanian et al. [2009] Balasubramanian, V.N., Gouripeddi, R., Panchanathan, S., Vermillion, J., Bhaskaran, A., Siegel, R.: Support vector machine based conformal predictors for risk of complications following a coronary drug eluting stent procedure. In: 2009 36th Annual Computers in Cardiology Conference (CinC), pp. 5–8 (2009). IEEE Narteni et al. [2023] Narteni, S., Carlevaro, A., Dabbene, F., Muselli, M., Mongelli, M.: Confiderai: Conformal interpretable-by-design score function for explainable and reliable artificial intelligence. In: Conformal and Probabilistic Prediction with Applications, pp. 485–487 (2023) Angelopoulos et al. [2020] Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. 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[2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Fontana, M., Zeni, G., Vantini, S.: Conformal prediction: A unified review of theory and new challenges. Bernoulli 29(1), 1–23 (2023) https://doi.org/10.3150/21-BEJ1447 Toccaceli [2022] Toccaceli, P.: Introduction to conformal predictors. Pattern Recognition 124, 108507 (2022) https://doi.org/10.1016/j.patcog.2021.108507 Forreryd et al. [2018] Forreryd, A., Norinder, U., Lindberg, T., Lindstedt, M.: Predicting skin sensitizers with confidence — using conformal prediction to determine applicability domain of gard. Toxicology in Vitro 48, 179–187 (2018) https://doi.org/10.1016/j.tiv.2018.01.021 Balasubramanian et al. [2009] Balasubramanian, V.N., Gouripeddi, R., Panchanathan, S., Vermillion, J., Bhaskaran, A., Siegel, R.: Support vector machine based conformal predictors for risk of complications following a coronary drug eluting stent procedure. In: 2009 36th Annual Computers in Cardiology Conference (CinC), pp. 5–8 (2009). IEEE Narteni et al. [2023] Narteni, S., Carlevaro, A., Dabbene, F., Muselli, M., Mongelli, M.: Confiderai: Conformal interpretable-by-design score function for explainable and reliable artificial intelligence. In: Conformal and Probabilistic Prediction with Applications, pp. 485–487 (2023) Angelopoulos et al. [2020] Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Toccaceli, P.: Introduction to conformal predictors. Pattern Recognition 124, 108507 (2022) https://doi.org/10.1016/j.patcog.2021.108507 Forreryd et al. [2018] Forreryd, A., Norinder, U., Lindberg, T., Lindstedt, M.: Predicting skin sensitizers with confidence — using conformal prediction to determine applicability domain of gard. Toxicology in Vitro 48, 179–187 (2018) https://doi.org/10.1016/j.tiv.2018.01.021 Balasubramanian et al. [2009] Balasubramanian, V.N., Gouripeddi, R., Panchanathan, S., Vermillion, J., Bhaskaran, A., Siegel, R.: Support vector machine based conformal predictors for risk of complications following a coronary drug eluting stent procedure. In: 2009 36th Annual Computers in Cardiology Conference (CinC), pp. 5–8 (2009). IEEE Narteni et al. [2023] Narteni, S., Carlevaro, A., Dabbene, F., Muselli, M., Mongelli, M.: Confiderai: Conformal interpretable-by-design score function for explainable and reliable artificial intelligence. In: Conformal and Probabilistic Prediction with Applications, pp. 485–487 (2023) Angelopoulos et al. [2020] Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Forreryd, A., Norinder, U., Lindberg, T., Lindstedt, M.: Predicting skin sensitizers with confidence — using conformal prediction to determine applicability domain of gard. Toxicology in Vitro 48, 179–187 (2018) https://doi.org/10.1016/j.tiv.2018.01.021 Balasubramanian et al. [2009] Balasubramanian, V.N., Gouripeddi, R., Panchanathan, S., Vermillion, J., Bhaskaran, A., Siegel, R.: Support vector machine based conformal predictors for risk of complications following a coronary drug eluting stent procedure. In: 2009 36th Annual Computers in Cardiology Conference (CinC), pp. 5–8 (2009). IEEE Narteni et al. [2023] Narteni, S., Carlevaro, A., Dabbene, F., Muselli, M., Mongelli, M.: Confiderai: Conformal interpretable-by-design score function for explainable and reliable artificial intelligence. In: Conformal and Probabilistic Prediction with Applications, pp. 485–487 (2023) Angelopoulos et al. [2020] Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Balasubramanian, V.N., Gouripeddi, R., Panchanathan, S., Vermillion, J., Bhaskaran, A., Siegel, R.: Support vector machine based conformal predictors for risk of complications following a coronary drug eluting stent procedure. In: 2009 36th Annual Computers in Cardiology Conference (CinC), pp. 5–8 (2009). IEEE Narteni et al. [2023] Narteni, S., Carlevaro, A., Dabbene, F., Muselli, M., Mongelli, M.: Confiderai: Conformal interpretable-by-design score function for explainable and reliable artificial intelligence. In: Conformal and Probabilistic Prediction with Applications, pp. 485–487 (2023) Angelopoulos et al. [2020] Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Narteni, S., Carlevaro, A., Dabbene, F., Muselli, M., Mongelli, M.: Confiderai: Conformal interpretable-by-design score function for explainable and reliable artificial intelligence. In: Conformal and Probabilistic Prediction with Applications, pp. 485–487 (2023) Angelopoulos et al. [2020] Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. 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Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299
- Fontana, M., Zeni, G., Vantini, S.: Conformal prediction: A unified review of theory and new challenges. Bernoulli 29(1), 1–23 (2023) https://doi.org/10.3150/21-BEJ1447 Toccaceli [2022] Toccaceli, P.: Introduction to conformal predictors. Pattern Recognition 124, 108507 (2022) https://doi.org/10.1016/j.patcog.2021.108507 Forreryd et al. [2018] Forreryd, A., Norinder, U., Lindberg, T., Lindstedt, M.: Predicting skin sensitizers with confidence — using conformal prediction to determine applicability domain of gard. Toxicology in Vitro 48, 179–187 (2018) https://doi.org/10.1016/j.tiv.2018.01.021 Balasubramanian et al. [2009] Balasubramanian, V.N., Gouripeddi, R., Panchanathan, S., Vermillion, J., Bhaskaran, A., Siegel, R.: Support vector machine based conformal predictors for risk of complications following a coronary drug eluting stent procedure. In: 2009 36th Annual Computers in Cardiology Conference (CinC), pp. 5–8 (2009). IEEE Narteni et al. [2023] Narteni, S., Carlevaro, A., Dabbene, F., Muselli, M., Mongelli, M.: Confiderai: Conformal interpretable-by-design score function for explainable and reliable artificial intelligence. In: Conformal and Probabilistic Prediction with Applications, pp. 485–487 (2023) Angelopoulos et al. [2020] Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Toccaceli, P.: Introduction to conformal predictors. Pattern Recognition 124, 108507 (2022) https://doi.org/10.1016/j.patcog.2021.108507 Forreryd et al. [2018] Forreryd, A., Norinder, U., Lindberg, T., Lindstedt, M.: Predicting skin sensitizers with confidence — using conformal prediction to determine applicability domain of gard. Toxicology in Vitro 48, 179–187 (2018) https://doi.org/10.1016/j.tiv.2018.01.021 Balasubramanian et al. [2009] Balasubramanian, V.N., Gouripeddi, R., Panchanathan, S., Vermillion, J., Bhaskaran, A., Siegel, R.: Support vector machine based conformal predictors for risk of complications following a coronary drug eluting stent procedure. In: 2009 36th Annual Computers in Cardiology Conference (CinC), pp. 5–8 (2009). IEEE Narteni et al. [2023] Narteni, S., Carlevaro, A., Dabbene, F., Muselli, M., Mongelli, M.: Confiderai: Conformal interpretable-by-design score function for explainable and reliable artificial intelligence. In: Conformal and Probabilistic Prediction with Applications, pp. 485–487 (2023) Angelopoulos et al. [2020] Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Forreryd, A., Norinder, U., Lindberg, T., Lindstedt, M.: Predicting skin sensitizers with confidence — using conformal prediction to determine applicability domain of gard. Toxicology in Vitro 48, 179–187 (2018) https://doi.org/10.1016/j.tiv.2018.01.021 Balasubramanian et al. [2009] Balasubramanian, V.N., Gouripeddi, R., Panchanathan, S., Vermillion, J., Bhaskaran, A., Siegel, R.: Support vector machine based conformal predictors for risk of complications following a coronary drug eluting stent procedure. In: 2009 36th Annual Computers in Cardiology Conference (CinC), pp. 5–8 (2009). IEEE Narteni et al. [2023] Narteni, S., Carlevaro, A., Dabbene, F., Muselli, M., Mongelli, M.: Confiderai: Conformal interpretable-by-design score function for explainable and reliable artificial intelligence. In: Conformal and Probabilistic Prediction with Applications, pp. 485–487 (2023) Angelopoulos et al. [2020] Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Balasubramanian, V.N., Gouripeddi, R., Panchanathan, S., Vermillion, J., Bhaskaran, A., Siegel, R.: Support vector machine based conformal predictors for risk of complications following a coronary drug eluting stent procedure. In: 2009 36th Annual Computers in Cardiology Conference (CinC), pp. 5–8 (2009). IEEE Narteni et al. [2023] Narteni, S., Carlevaro, A., Dabbene, F., Muselli, M., Mongelli, M.: Confiderai: Conformal interpretable-by-design score function for explainable and reliable artificial intelligence. In: Conformal and Probabilistic Prediction with Applications, pp. 485–487 (2023) Angelopoulos et al. [2020] Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Narteni, S., Carlevaro, A., Dabbene, F., Muselli, M., Mongelli, M.: Confiderai: Conformal interpretable-by-design score function for explainable and reliable artificial intelligence. In: Conformal and Probabilistic Prediction with Applications, pp. 485–487 (2023) Angelopoulos et al. [2020] Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. 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PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. 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- Toccaceli, P.: Introduction to conformal predictors. Pattern Recognition 124, 108507 (2022) https://doi.org/10.1016/j.patcog.2021.108507 Forreryd et al. [2018] Forreryd, A., Norinder, U., Lindberg, T., Lindstedt, M.: Predicting skin sensitizers with confidence — using conformal prediction to determine applicability domain of gard. Toxicology in Vitro 48, 179–187 (2018) https://doi.org/10.1016/j.tiv.2018.01.021 Balasubramanian et al. [2009] Balasubramanian, V.N., Gouripeddi, R., Panchanathan, S., Vermillion, J., Bhaskaran, A., Siegel, R.: Support vector machine based conformal predictors for risk of complications following a coronary drug eluting stent procedure. In: 2009 36th Annual Computers in Cardiology Conference (CinC), pp. 5–8 (2009). IEEE Narteni et al. [2023] Narteni, S., Carlevaro, A., Dabbene, F., Muselli, M., Mongelli, M.: Confiderai: Conformal interpretable-by-design score function for explainable and reliable artificial intelligence. 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Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Forreryd, A., Norinder, U., Lindberg, T., Lindstedt, M.: Predicting skin sensitizers with confidence — using conformal prediction to determine applicability domain of gard. Toxicology in Vitro 48, 179–187 (2018) https://doi.org/10.1016/j.tiv.2018.01.021 Balasubramanian et al. [2009] Balasubramanian, V.N., Gouripeddi, R., Panchanathan, S., Vermillion, J., Bhaskaran, A., Siegel, R.: Support vector machine based conformal predictors for risk of complications following a coronary drug eluting stent procedure. In: 2009 36th Annual Computers in Cardiology Conference (CinC), pp. 5–8 (2009). IEEE Narteni et al. [2023] Narteni, S., Carlevaro, A., Dabbene, F., Muselli, M., Mongelli, M.: Confiderai: Conformal interpretable-by-design score function for explainable and reliable artificial intelligence. In: Conformal and Probabilistic Prediction with Applications, pp. 485–487 (2023) Angelopoulos et al. [2020] Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Balasubramanian, V.N., Gouripeddi, R., Panchanathan, S., Vermillion, J., Bhaskaran, A., Siegel, R.: Support vector machine based conformal predictors for risk of complications following a coronary drug eluting stent procedure. In: 2009 36th Annual Computers in Cardiology Conference (CinC), pp. 5–8 (2009). IEEE Narteni et al. [2023] Narteni, S., Carlevaro, A., Dabbene, F., Muselli, M., Mongelli, M.: Confiderai: Conformal interpretable-by-design score function for explainable and reliable artificial intelligence. In: Conformal and Probabilistic Prediction with Applications, pp. 485–487 (2023) Angelopoulos et al. [2020] Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Narteni, S., Carlevaro, A., Dabbene, F., Muselli, M., Mongelli, M.: Confiderai: Conformal interpretable-by-design score function for explainable and reliable artificial intelligence. In: Conformal and Probabilistic Prediction with Applications, pp. 485–487 (2023) Angelopoulos et al. [2020] Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. 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[2020] Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. 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Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Balasubramanian, V.N., Gouripeddi, R., Panchanathan, S., Vermillion, J., Bhaskaran, A., Siegel, R.: Support vector machine based conformal predictors for risk of complications following a coronary drug eluting stent procedure. In: 2009 36th Annual Computers in Cardiology Conference (CinC), pp. 5–8 (2009). IEEE Narteni et al. [2023] Narteni, S., Carlevaro, A., Dabbene, F., Muselli, M., Mongelli, M.: Confiderai: Conformal interpretable-by-design score function for explainable and reliable artificial intelligence. In: Conformal and Probabilistic Prediction with Applications, pp. 485–487 (2023) Angelopoulos et al. [2020] Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Narteni, S., Carlevaro, A., Dabbene, F., Muselli, M., Mongelli, M.: Confiderai: Conformal interpretable-by-design score function for explainable and reliable artificial intelligence. In: Conformal and Probabilistic Prediction with Applications, pp. 485–487 (2023) Angelopoulos et al. [2020] Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. 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International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. 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In: Conformal and Probabilistic Prediction with Applications, pp. 485–487 (2023) Angelopoulos et al. [2020] Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. 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Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. 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Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. 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PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. 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PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. 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International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. 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[2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299
- Angelopoulos, A.N., Bates, S., Jordan, M., Malik, J.: Uncertainty sets for image classifiers using conformal prediction. In: International Conference on Learning Representations (2020) Park et al. [2019] Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. 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Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Park, S., Bastani, O., Matni, N., Lee, I.: Pac confidence sets for deep neural networks via calibrated prediction. In: International Conference on Learning Representations (2019) Andéol et al. [2024] Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. 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International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299
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(2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299
- Andéol, L., Fel, T., De Grancey, F., Mossina, L.: Conformal prediction for trustworthy detection of railway signals. AI and Ethics, 1–5 (2024) Carlevaro et al. [2023] Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299
- Carlevaro, A., Alamo, T., Dabbene, F., Mongelli, M.: Probabilistic safety regions via finite families of scalable classifiers (2023) arXiv:2309.04627 [stat.ML] Chzhen et al. [2021] Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Chzhen, E., Denis, C., Hebiri, M., Lorieul, T.: Set-valued classification–overview via a unified framework (2021) arXiv:2102.12318 [stat.ML] Lenatti et al. [2022] Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. 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In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. 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IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. 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Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. 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IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299
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ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299
- Lenatti, M., Carlevaro, A., Guergachi, A., Keshavjee, K., Mongelli, M., Paglialonga, A.: A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations. PLOS ONE 17(11), 1–24 (2022) https://doi.org/10.1371/journal.pone.0272825 Carlevaro et al. [2022] Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. 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International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. 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PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. 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International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. 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- Carlevaro, A., Lenatti, M., Paglialonga, A., Mongelli, M.: Counterfactual building and evaluation via explainable support vector data description. IEEE Access 10, 60849–60861 (2022) https://doi.org/10.1109/ACCESS.2022.3180026 Vovk et al. [1999] Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. 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PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99, pp. 444–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999) Hüllermeier and Waegeman [2021] Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning 110, 457–506 (2021) https://doi.org/10.1007/s10994-021-05946-3 Sale et al. [2023] Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. 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PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. 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PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. 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International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. 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[2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299
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IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299
- Sale, Y., Caprio, M., Hüllermeier, E.: Is the volume of a credal set a good measure for epistemic uncertainty? (2023) arXiv:2306.09586 [cs.LG] Abellán et al. [2006] Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299
- Abellán, J., Klir, G.J., Moral, S.: Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1), 29–44 (2006) Sale et al. [2023] Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299
- Sale, Y., Bengs, V., Caprio, M., Hüllermeier, E.: Second-order uncertainty quantification: A distance-based approach (2023) arXiv:2312.00995 [cs.LG] Valiant [2013] Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299
- Valiant, L.: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. Basic Books, Inc., USA (2013) Park et al. [2022] Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299
- Park, S., Dobriban, E., Lee, I., Bastani, O.: PAC prediction sets for meta-learning. Advances in Neural Information Processing Systems 35, 37920–37931 (2022) Vovk [2012] Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299
- Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) Proceedings of the Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 25, pp. 475–490. PMLR, Singapore Management University, Singapore (2012). https://proceedings.mlr.press/v25/vovk12.html Aiello et al. [2015] Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299
- Aiello, M., Mongelli, M., Papaleo, G.: DNS tunneling detection through statistical fingerprints of protocol messages and machine learning. International Journal of Communication Systems 28(14), 1987–2002 (2015) Carlevaro and Mongelli [2021] Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299
- Carlevaro, A., Mongelli, M.: A new SVDD approach to reliable and eXplainable AI. IEEE Intelligent Systems (2021) https://doi.org/10.1109/MIS.2021.3123669 Vaccari et al. [2022] Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299 Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299
- Vaccari, I., Carlevaro, A., Narteni, S., Cambiaso, E., Mongelli, M.: eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10, 83949–83970 (2022) https://doi.org/10.1109/ACCESS.2022.3197299
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