Papers
Topics
Authors
Recent
Detailed Answer
Quick Answer
Concise responses based on abstracts only
Detailed Answer
Well-researched responses based on abstracts and relevant paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses
Gemini 2.5 Flash
Gemini 2.5 Flash 64 tok/s
Gemini 2.5 Pro 40 tok/s Pro
GPT-5 Medium 17 tok/s Pro
GPT-5 High 13 tok/s Pro
GPT-4o 86 tok/s Pro
Kimi K2 208 tok/s Pro
GPT OSS 120B 457 tok/s Pro
Claude Sonnet 4 32 tok/s Pro
2000 character limit reached

Algorithms for Caching and MTS with reduced number of predictions (2404.06280v2)

Published 9 Apr 2024 in cs.LG and cs.DS

Abstract: ML-augmented algorithms utilize predictions to achieve performance beyond their worst-case bounds. Producing these predictions might be a costly operation -- this motivated Im et al. '22 to introduce the study of algorithms which use predictions parsimoniously. We design parsimonious algorithms for caching and MTS with action predictions, proposed by Antoniadis et al. '20, focusing on the parameters of consistency (performance with perfect predictions) and smoothness (dependence of their performance on the prediction error). Our algorithm for caching is 1-consistent, robust, and its smoothness deteriorates with the decreasing number of available predictions. We propose an algorithm for general MTS whose consistency and smoothness both scale linearly with the decreasing number of predictions. Without the restriction on the number of available predictions, both algorithms match the earlier guarantees achieved by Antoniadis et al. '20.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (55)
  1. The dynamics of repeat consumption. In Proceedings of conference World Wide Web ’14, pages 419–430, 2014. doi: 10.1145/2566486.2568018.
  2. Secretary and online matching problems with machine learned advice. In NeurIPS, 2020.
  3. Learning-augmented dynamic power management with multiple states via new ski rental bounds. In NeurIPS, 2021.
  4. Paging with succinct predictions, 2022a.
  5. A novel prediction setup for online speed-scaling. In SWAT, volume 227 of LIPIcs, pages 9:1–9:20. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022b.
  6. Online metric algorithms with untrusted predictions. ACM Trans. Algorithms, 19(2), apr 2023. ISSN 1549-6325. doi: 10.1145/3582689. URL https://doi.org/10.1145/3582689.
  7. Flow time scheduling with uncertain processing time. In Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing, STOC 2021, page 1070–1080, New York, NY, USA, 2021. Association for Computing Machinery. ISBN 9781450380539. doi: 10.1145/3406325.3451023. URL https://doi.org/10.1145/3406325.3451023.
  8. Distortion-oblivious algorithms for minimizing flow time. In Proceedings of the 2022 ACM-SIAM Symposium on Discrete Algorithms, SODA 2022, Virtual Conference / Alexandria, VA, USA, January 9 - 12, 2022, pages 252–274. SIAM, 2022. URL https://doi.org/10.1137/1.9781611977073.13.
  9. Learning augmented energy minimization via speed scaling. In NeurIPS, 2020.
  10. Learning-augmented weighted paging. In SODA, 2022.
  11. Ramsey-type theorems for metric spaces with applications to online problems. J. Comput. Syst. Sci., 72(5):890–921, 2006.
  12. L. A. Belady. A study of replacement algorithms for virtual-storage computer. IBM Syst. J., 5(2):78–101, 1966. doi: 10.1147/sj.52.0078. URL https://doi.org/10.1147/sj.52.0078.
  13. A universal error measure for input predictions applied to online graph problems. CoRR, abs/2205.12850, 2022. doi: 10.48550/arXiv.2205.12850. URL https://doi.org/10.48550/arXiv.2205.12850.
  14. A. Blum and C. Burch. On-line learning and the metrical task system problem. Mach. Learn., 39(1):35–58, 2000. doi: 10.1023/A:1007621832648.
  15. Online algorithms with advice: The tape model. Inf. Comput., 254:59–83, 2017.
  16. A. Borodin and R. El-Yaniv. Online computation and competitive analysis. Cambridge University Press, 1998. ISBN 978-0-521-56392-5.
  17. An optimal on-line algorithm for metrical task system. J. ACM, 39(4):745–763, 1992. doi: 10.1145/146585.146588.
  18. Online Algorithms with Advice: A Survey. ACM Computing Surveys, 50(2):1–34, 2017. Article No. 19.
  19. Online unit profit knapsack with untrusted predictions. In SWAT, volume 227 of LIPIcs, pages 20:1–20:17. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022.
  20. Metrical task systems on trees via mirror descent and unfair gluing. In Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2019, pages 89–97, 2019. doi: 10.1137/1.9781611975482.6. URL https://doi.org/10.1137/1.9781611975482.6.
  21. Faster fundamental graph algorithms via learned predictions. In Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pages 3583–3602. PMLR, 17–23 Jul 2022. URL https://proceedings.mlr.press/v162/chen22v.html.
  22. Robust learning-augmented caching: An experimental study. In M. Meila and T. Zhang, editors, Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, volume 139 of Proceedings of Machine Learning Research, pages 1920–1930. PMLR, 2021. URL http://proceedings.mlr.press/v139/chledowski21a.html.
  23. Friendship and mobility: user movement in location-based social networks. In Proceedings of SIGKDD’11, pages 1082–1090, 2011. doi: 10.1145/2020408.2020579. URL https://snap.stanford.edu/data/loc-brightkite.html.
  24. CitiBike. Citi bike trip histories. https://www.citibikenyc.com/system-data.
  25. Faster matchings via learned duals. In NeurIPS, pages 10393–10406, 2021.
  26. Measuring the problem-relevant information in input. RAIRO - Theor. Inf. Appl., 43(3):585–613, 2009.
  27. Online algorithms with costly predictions. In F. Ruiz, J. Dy, and J.-W. van de Meent, editors, Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, volume 206 of Proceedings of Machine Learning Research, pages 8078–8101. PMLR, 25–27 Apr 2023. URL https://proceedings.mlr.press/v206/drygala23a.html.
  28. Secretaries with advice. In EC, pages 409–429. ACM, 2021.
  29. Robustification of online graph exploration methods. In AAAI, pages 9732–9740. AAAI Press, 2022.
  30. Online computation with advice. In Automata, Languages and Programming, 36th International Colloquium, ICALP 2009, Proceedings, Part I, volume 5555 of Lecture Notes in Computer Science, pages 427–438. Springer, 2009. URL https://doi.org/10.1007/978-3-642-02927-1_36.
  31. Learning-augmented k-means clustering. In The Tenth International Conference on Learning Representations, ICLR 2022. OpenReview.net, 2022. URL https://openreview.net/forum?id=X8cLTHexYyY.
  32. Competitive paging algorithms. Journal of Algorithms, 12(4):685–699, 1991. ISSN 0196-6774. doi: https://doi.org/10.1016/0196-6774(91)90041-V. URL https://www.sciencedirect.com/science/article/pii/019667749190041V.
  33. Augmenting online algorithms with \varepsilon-accurate predictions. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, editors, Advances in Neural Information Processing Systems, volume 35, pages 2115–2127. Curran Associates, Inc., 2022. URL https://proceedings.neurips.cc/paper_files/paper/2022/file/0ea048312aa812b2711fe765a9e9ef05-Paper-Conference.pdf.
  34. Information complexity of online problems. In MFCS, volume 6281 of LNCS, pages 24–36. Springer, 2010.
  35. Online knapsack with frequency predictions. In NeurIPS, pages 2733–2743, 2021.
  36. Parsimonious learning-augmented caching. In ICML, 2022.
  37. Online page migration with ml advice. In G. Camps-Valls, F. J. R. Ruiz, and I. Valera, editors, Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, volume 151 of Proceedings of Machine Learning Research, pages 1655–1670. PMLR, 28–30 Mar 2022. URL https://proceedings.mlr.press/v151/indyk22a.html.
  38. A. Jain and C. Lin. Back to the future: Leveraging belady’s algorithm for improved cache replacement. SIGARCH Comput. Archit. News, 44(3):78–89, June 2016. ISSN 0163-5964. doi: 10.1145/3007787.3001146. URL https://doi.org/10.1145/3007787.3001146.
  39. Online algorithms for weighted paging with predictions. ACM Trans. Algorithms, 18(4):39:1–39:27, 2022. doi: 10.1145/3548774. URL https://doi.org/10.1145/3548774.
  40. The case for learned index structures. In Proceedings of SIGMOD’18, pages 489–504, 2018. doi: 10.1145/3183713.3196909.
  41. Online scheduling via learned weights. In Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms, SODA 2020, pages 1859–1877. SIAM, 2020. URL https://doi.org/10.1137/1.9781611975994.114.
  42. A. Lindermayr and N. Megow. Permutation predictions for non-clairvoyant scheduling. In SPAA, pages 357–368. ACM, 2022a.
  43. A. Lindermayr and N. Megow. Algorithms with predictions. https://algorithms-with-predictions.github.io, 2022b. URL https://algorithms-with-predictions.github.io. [Online; accessed 8-September-2022].
  44. Double coverage with machine-learned advice. In M. Braverman, editor, 13th Innovations in Theoretical Computer Science Conference, ITCS 2022, January 31 - February 3, 2022, Berkeley, CA, USA, volume 215 of LIPIcs, pages 99:1–99:18. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022. doi: 10.4230/LIPIcs.ITCS.2022.99. URL https://doi.org/10.4230/LIPIcs.ITCS.2022.99.
  45. An imitation learning approach for cache replacement. In Proceedings of the 37th International Conference on Machine Learning, volume 119 of Proceedings of Machine Learning Research, pages 6237–6247. PMLR, 13–18 Jul 2020. URL https://proceedings.mlr.press/v119/liu20f.html.
  46. T. Lykouris and S. Vassilvitskii. Competitive caching with machine learned advice. J. ACM, 68(4):24:1–24:25, 2021.
  47. M. Mitzenmacher and S. Vassilvitskii. Algorithms with predictions. In Beyond the Worst-Case Analysis of Algorithms, pages 646–662. Cambridge University Press, 2020.
  48. A. Polak and M. Zub. Learning-augmented maximum flow. CoRR, abs/2207.12911, 2022.
  49. Improving online algorithms via ML predictions. In NeurIPS, 2018.
  50. D. Rohatgi. Near-optimal bounds for online caching with machine learned advice. In SODA, 2020.
  51. S. Sakaue and T. Oki. Discrete-convex-analysis-based framework for warm-starting algorithms with predictions. CoRR, abs/2205.09961, 2022. doi: 10.48550/arXiv.2205.09961.
  52. Applying deep learning to the cache replacement problem. In Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture, MICRO ’52, page 413–425, New York, NY, USA, 2019. Association for Computing Machinery. ISBN 9781450369381. doi: 10.1145/3352460.3358319. URL https://doi.org/10.1145/3352460.3358319.
  53. Amortized efficiency of list update and paging rules. Commun. ACM, 28(2):202–208, 1985. doi: 10.1145/2786.2793.
  54. A. Wei. Better and simpler learning-augmented online caching. In APPROX/RANDOM, 2020.
  55. Data-driven competitive algorithms for online knapsack and set cover. In AAAI, pages 10833–10841. AAAI Press, 2021.
Citations (4)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up
Lightbulb On Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now

Don't miss out on important new AI/ML research

See which papers are being discussed right now on X, Reddit, and more:

Explore Trending Papers

“Emergent Mind helps me see which AI papers have caught fire online.”

Philip

Philip

Creator, AI Explained on YouTube