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Outlier Gradient Analysis: Efficiently Identifying Detrimental Training Samples for Deep Learning Models (2405.03869v4)

Published 6 May 2024 in cs.LG and cs.AI

Abstract: A core data-centric learning challenge is the identification of training samples that are detrimental to model performance. Influence functions serve as a prominent tool for this task and offer a robust framework for assessing training data influence on model predictions. Despite their widespread use, their high computational cost associated with calculating the inverse of the Hessian matrix pose constraints, particularly when analyzing large-sized deep models. In this paper, we establish a bridge between identifying detrimental training samples via influence functions and outlier gradient detection. This transformation not only presents a straightforward and Hessian-free formulation but also provides insights into the role of the gradient in sample impact. Through systematic empirical evaluations, we first validate the hypothesis of our proposed outlier gradient analysis approach on synthetic datasets. We then demonstrate its effectiveness in detecting mislabeled samples in vision models and selecting data samples for improving performance of natural language processing transformer models. We also extend its use to influential sample identification for fine-tuning LLMs.

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References (64)
  1. Dmlr: Data-centric machine learning research–past, present and future. arXiv preprint arXiv:2311.13028, 2023.
  2. Data-centric artificial intelligence: A survey. arXiv preprint arXiv:2303.10158, 2023.
  3. What Data Benefits My Classifier? Enhancing Model Performance and Interpretability through Influence-Based Data Selection. In International Conference on Learning Representations, 2024.
  4. DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models. In International Conference on Learning Representations, 2024.
  5. Training data influence analysis and estimation: A survey. arXiv preprint arXiv:2212.04612, 2022.
  6. Residuals and influence in regression. New York: Chapman and Hall, 1982.
  7. Data shapley: Equitable valuation of data for machine learning. In International Conference on Machine Learning, 2019.
  8. Towards efficient data valuation based on the shapley value. In International Conference on Artificial Intelligence and Statistics, 2019.
  9. Understanding black-box predictions via influence functions. In International Conference on Machine Learning, 2017.
  10. Influence Functions in Deep Learning Are Fragile. In International Conference on Learning Representations, 2020a.
  11. Best subset selection via a modern optimization lens. The Annals of Statistics, 2016.
  12. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692, 2019.
  13. Beta Shapley: a unified and noise-reduced data valuation framework for machine learning. In International Conference on Artificial Intelligence and Statistics, 2022.
  14. Efficient task specific data valuation for nearest neighbor algorithms. Proceedings of the VLDB Endowment, 2018.
  15. Second-order stochastic optimization for machine learning in linear time. The Journal of Machine Learning Research, 2017.
  16. Representer point selection for explaining deep neural networks. Advances in Neural Information Processing Systems, 2018.
  17. Hydra: Hypergradient data relevance analysis for interpreting deep neural networks. In AAAI Conference on Artificial Intelligence, 2021.
  18. Scaling up influence functions. In AAAI Conference on Artificial Intelligence, 2022.
  19. What neural networks memorize and why: Discovering the long tail via influence estimation. Advances in Neural Information Processing Systems, 2020.
  20. Estimating training data influence by tracing gradient descent. Advances in Neural Information Processing Systems, 2020.
  21. Input similarity from the neural network perspective. Advances in Neural Information Processing Systems, 2019.
  22. Resolving training biases via influence-based data relabeling. In International Conference on Learning Representations, 2021.
  23. Studying large language model generalization with influence functions. arXiv preprint arXiv:2308.03296, 2023.
  24. Theoretical and practical perspectives on what influence functions do. Advances in Neural Information Processing Systems, 2024.
  25. Make every example count: On the stability and utility of self-influence for learning from noisy nlp datasets. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023.
  26. Self-influence guided data reweighting for language model pre-training. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023.
  27. Datamodels: Predicting predictions from training data. arXiv preprint arXiv:2202.00622, 2022.
  28. Efficient Data Subset Selection to Generalize Training Across Models: Transductive and Inductive Networks. In Advances in Neural Information Processing Systems, 2023.
  29. Deep learning on a data diet: Finding important examples early in training. In Advances in Neural Information Processing Systems, 2021.
  30. Retrieve: Coreset selection for efficient and robust semi-supervised learning. Advances in Neural Information Processing Systems, 2021.
  31. Data pruning via moving-one-sample-out. Advances in Neural Information Processing Systems, 36, 2024.
  32. Dataset pruning: Reducing training data by examining generalization influence. In The Eleventh International Conference on Learning Representations, 2022.
  33. Deeper understanding of black-box predictions via generalized influence functions. arXiv preprint arXiv:2312.05586, 2023.
  34. Fair clustering using antidote data. In Algorithmic Fairness through the Lens of Causality and Robustness Workshop, 2022.
  35. Learning antidote data to individual unfairness. In International Conference on Machine Learning, 2023.
  36. Mark A Hall. Correlation-based feature selection for machine learning. PhD thesis, The University of Waikato, 1999.
  37. Feature selection in machine learning: A new perspective. Neurocomputing, 2018.
  38. Active learning with statistical models. Journal of Artificial Intelligence Research, 1996.
  39. Influence selection for active learning. In IEEE/CVF International Conference on Computer Vision, 2021.
  40. How to measure uncertainty in uncertainty sampling for active learning. Machine Learning, 2022.
  41. Submodularity in data subset selection and active learning. In International Conference on Machine Learning, 2015.
  42. Poisoning attacks on algorithmic fairness. In Machine Learning and Knowledge Discovery in Databases: European Conference, 2021.
  43. Exacerbating algorithmic bias through fairness attacks. In AAAI Conference on Artificial Intelligence, 2021.
  44. Robust fair clustering: A novel fairness attack and defense framework. In International Conference on Learning Representations, 2023.
  45. Training data attribution for diffusion models. arXiv preprint arXiv:2306.02174, 2023.
  46. Frank R Hampel. The influence curve and its role in robust estimation. Journal of the american statistical association, 1974.
  47. Influence functionals for time series. The Annals of Statistics, 1986.
  48. Explaining black box predictions and unveiling data artifacts through influence functions. In Annual Meeting of the Association for Computational Linguistics, 2020.
  49. On second-order group influence functions for black-box predictions. In International Conference on Machine Learning, 2020b.
  50. Ahmed Alaa and Mihaela Van Der Schaar. Discriminative jackknife: Quantifying uncertainty in deep learning via higher-order influence functions. In International Conference on Machine Learning, 2020.
  51. Isolation forest. In IEEE International Conference on Data Mining, 2008.
  52. Distance-based outliers: algorithms and applications. The VLDB Journal, 2000.
  53. Confident learning: Estimating uncertainty in dataset labels. Journal of Artificial Intelligence Research, 2021.
  54. Identifying mislabeled instances in classification datasets. In International Joint Conference on Neural Networks, 2019.
  55. Model-agnostic label quality scoring to detect real-world label errors. In ICML DataPerf Workshop, 2022.
  56. Learning with noisy labels revisited: A study using real-world human annotations. In International Conference on Learning Representations, 2022.
  57. Learning multiple layers of features from tiny images. University of Toronto, 2009.
  58. Deep residual learning for image recognition. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2016.
  59. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. Ieee, 2009.
  60. GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding. arXiv preprint arXiv:1804.07461., 2018.
  61. LoRA: Low-Rank Adaptation of Large Language Models. In International Conference on Learning Representations, 2022.
  62. Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the 2013 conference on empirical methods in natural language processing, pages 1631–1642, 2013.
  63. Automatically constructing a corpus of sentential paraphrases. In Third International Workshop on Paraphrasing (IWP2005), 2005.
  64. Very sparse random projections. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 287–296, 2006.

Summary

  • The paper introduces a novel Outlier Gradient Analysis method that reinterprets influence functions through gradient outlier detection.
  • It significantly reduces computational cost by bypassing Hessian matrix inversion and leveraging readily available first-order information.
  • Empirical results across vision, NLP, and LLMs demonstrate enhanced detection of detrimental samples with superior AUC and Recall scores.

Unveiling a Streamlined Approach for Identifying Influential Data Samples Using Outlier Gradient Analysis

The Challenge with Traditional Influence Functions

Influence functions have been a cornerstone in data-centric AI learning, enabling researchers and practitioners to understand and optimize the impact of individual data samples on model behavior without the need for costly retraining. Traditionally, this approach required dealing with complex mathematical calculations like the Hessian matrix's inverse, which is computationally intensive and often impracticable for large, deep models.

Moreover, the method is constrained by its assumptions about the model's convexity, limiting its application across various modern non-convex models like deep neural networks.

A Novel Proposition: Outlier Gradient Analysis

A paper addresses these complications by proposing a transformative framework called Outlier Gradient Analysis. This paradigm shift involves an equivalence transformation that reinterprets the influence estimation problem through outlier detection in the gradient space. Simply put, the paper proposes to:

  • Simplify the computational process: By avoiding direct calculations involving the Hessian matrix, the method significantly reduces computational demands.
  • Extend applicability to non-convex models: It leverages first-order gradient information, which is readily available from the training process, thus bypassing the limitations set by the necessity for convexity in models.

Empirical Validation and Results

The accuracy and effectiveness of this new approach were empirically tested across several contexts:

  1. Synthetic Datasets: The method's conceptual soundness was validated using 2D toy datasets, where it was not only able to identify known detrimental samples accurately but also did so with remarkable computational efficiency.
  2. Vision Models: Tested on noisy CIFAR datasets, outlier gradient analysis showed superior performance in detecting and trimming mislabeled data samples compared to several existing methods.
  3. NLP Models: When used for selecting optimal subsets of data for fine-tuning transformer models like RoBERTa, the approach again proved to be beneficial and exhibited better performance in some cases compared to other influence-based methods.
  4. LLMs: The framework excelled in identifying influential training samples for LLMs tasked with text generation, achieving perfect scores in AUC and Recall metrics for class detection.

Practical Implications and Future Directions

The implications of such a streamlined approach are promising. For industries and applications where model training time and resources are critical constraints—such as in real-time systems and large-scale applications—this method offers a practical alternative. Moreover, its ability to perform efficiently across different types of models and data tasks—from image processing to natural language understanding—enhances its utility and adaptability.

Looking ahead, the potential expansions of this methodology could include further fine-tuning of the outlier detection techniques to enhance discriminant power or adapting the methods presented for use in unsupervised learning scenarios, which could revolutionize the way data influence is perceived and managed in AI development.

In conclusion, Outlier Gradient Analysis marks a significant step towards more accessible, efficient, and versatile methods in the field of data-centric machine learning. By simplifying and generalizing the way we estimate data influence, it not only addresses but effectively bypasses major limitations of previous methodologies, opening new avenues for research and application in artificial intelligence.

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