Learnable and Instance-Robust Predictions for Online Matching, Flows and Load Balancing

Authors Thomas Lavastida, Benjamin Moseley, R. Ravi, Chenyang Xu



PDF
Thumbnail PDF

File

LIPIcs.ESA.2021.59.pdf
  • Filesize: 0.72 MB
  • 17 pages

Document Identifiers

Author Details

Thomas Lavastida
  • Carnegie Mellon University, Pittsburgh, PA, USA
Benjamin Moseley
  • Carnegie Mellon University, Pittsburgh, PA, USA
R. Ravi
  • Carnegie Mellon University, Pittsburgh, PA, USA
Chenyang Xu
  • Zhejiang University, China

Cite AsGet BibTex

Thomas Lavastida, Benjamin Moseley, R. Ravi, and Chenyang Xu. Learnable and Instance-Robust Predictions for Online Matching, Flows and Load Balancing. In 29th Annual European Symposium on Algorithms (ESA 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 204, pp. 59:1-59:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)
https://doi.org/10.4230/LIPIcs.ESA.2021.59

Abstract

We propose a new model for augmenting algorithms with predictions by requiring that they are formally learnable and instance robust. Learnability ensures that predictions can be efficiently constructed from a reasonable amount of past data. Instance robustness ensures that the prediction is robust to modest changes in the problem input, where the measure of the change may be problem specific. Instance robustness insists on a smooth degradation in performance as a function of the change. Ideally, the performance is never worse than worst-case bounds. This also allows predictions to be objectively compared. We design online algorithms with predictions for a network flow allocation problem and restricted assignment makespan minimization. For both problems, two key properties are established: high quality predictions can be learned from a small sample of prior instances and these predictions are robust to errors that smoothly degrade as the underlying problem instance changes.

Subject Classification

ACM Subject Classification
  • Theory of computation → Online algorithms
Keywords
  • Learning-augmented algorithms
  • Online algorithms
  • Flow allocation

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads

References

  1. Shipra Agrawal, Zizhuo Wang, and Yinyu Ye. A dynamic near-optimal algorithm for online linear programming. Oper. Res., 62(4):876-890, 2014. URL: https://doi.org/10.1287/opre.2014.1289.
  2. Shipra Agrawal, Morteza Zadimoghaddam, and Vahab Mirrokni. Proportional allocation: Simple, distributed, and diverse matching with high entropy. In Jennifer Dy and Andreas Krause, editors, Proceedings of the 35th International Conference on Machine Learning, volume 80 of Proceedings of Machine Learning Research, pages 99-108, Stockholmsmässan, Stockholm Sweden, July 2018. PMLR. URL: http://proceedings.mlr.press/v80/agrawal18b.html.
  3. Keerti Anand, Rong Ge, and Debmalya Panigrahi. Customizing ml predictions for online algorithms. ICML 2020, 2020. Google Scholar
  4. Spyros Angelopoulos, Christoph Dürr, Shendan Jin, Shahin Kamali, and Marc P. Renault. Online computation with untrusted advice. In Thomas Vidick, editor, 11th Innovations in Theoretical Computer Science Conference, ITCS 2020, January 12-14, 2020, Seattle, Washington, USA, volume 151 of LIPIcs, pages 52:1-52:15. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020. URL: https://doi.org/10.4230/LIPIcs.ITCS.2020.52.
  5. Martin Anthony and Peter L. Bartlett. Neural Network Learning: Theoretical Foundations. Cambridge University Press, USA, 1st edition, 2009. Google Scholar
  6. Antonios Antoniadis, Christian Coester, Marek Eliás, Adam Polak, and Bertrand Simon. Online metric algorithms with untrusted predictions. CoRR, abs/2003.02144, 2020. URL: http://arxiv.org/abs/2003.02144.
  7. Antonios Antoniadis, Themis Gouleakis, Pieter Kleer, and Pavel Kolev. Secretary and online matching problems with machine learned advice. CoRR, abs/2006.01026, 2020. URL: http://arxiv.org/abs/2006.01026.
  8. Yossi Azar, Joseph Seffi Naor, and Raphael Rom. The competitiveness of on-line assignments. J. Algorithms, 18(2):221-237, 1995. URL: https://doi.org/10.1006/jagm.1995.1008.
  9. Maria-Florina Balcan, Dan F. DeBlasio, Travis Dick, Carl Kingsford, Tuomas Sandholm, and Ellen Vitercik. How much data is sufficient to learn high-performing algorithms? CoRR, abs/1908.02894, 2019. URL: http://arxiv.org/abs/1908.02894.
  10. Maria-Florina Balcan, Travis Dick, Tuomas Sandholm, and Ellen Vitercik. Learning to branch. In Jennifer G. Dy and Andreas Krause, editors, Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018, volume 80 of Proceedings of Machine Learning Research, pages 353-362. PMLR, 2018. URL: http://proceedings.mlr.press/v80/balcan18a.html.
  11. Maria-Florina Balcan, Travis Dick, and Ellen Vitercik. Dispersion for data-driven algorithm design, online learning, and private optimization. In Mikkel Thorup, editor, 59th IEEE Annual Symposium on Foundations of Computer Science, FOCS 2018, Paris, France, October 7-9, 2018, pages 603-614. IEEE Computer Society, 2018. URL: https://doi.org/10.1109/FOCS.2018.00064.
  12. Maria-Florina Balcan, Travis Dick, and Colin White. Data-driven clustering via parameterized lloyd’s families. In Samy Bengio, Hanna M. Wallach, Hugo Larochelle, Kristen Grauman, Nicolò Cesa-Bianchi, and Roman Garnett, editors, Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, 3-8 December 2018, Montréal, Canada, pages 10664-10674, 2018. URL: http://papers.nips.cc/paper/8263-data-driven-clustering-via-parameterized-lloyds-families.
  13. Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, and Manish Purohit. Online learning with imperfect hints. CoRR, abs/2002.04726, 2020. URL: http://arxiv.org/abs/2002.04726.
  14. Joan Boyar, Lene M. Favrholdt, Christian Kudahl, Kim S. Larsen, and Jesper W. Mikkelsen. Online algorithms with advice: A survey. SIGACT News, 47(3):93-129, 2016. URL: https://doi.org/10.1145/2993749.2993766.
  15. Shuchi Chawla, Evangelia Gergatsouli, Yifeng Teng, Christos Tzamos, and Ruimin Zhang. Pandora’s box with correlations: Learning and approximation, 2020. URL: http://arxiv.org/abs/1911.01632.
  16. Nikhil R. Devanur and Thomas P. Hayes. The adwords problem: online keyword matching with budgeted bidders under random permutations. In Proceedings 10th ACM Conference on Electronic Commerce (EC-2009), Stanford, California, USA, July 6-10, 2009, pages 71-78, 2009. Google Scholar
  17. Nikhil R. Devanur, Kamal Jain, and Robert D. Kleinberg. Randomized primal-dual analysis of RANKING for online bipartite matching. In Sanjeev Khanna, editor, Proceedings of the Twenty-Fourth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2013, New Orleans, Louisiana, USA, January 6-8, 2013, pages 101-107. SIAM, 2013. URL: https://doi.org/10.1137/1.9781611973105.7.
  18. Jon Feldman, Monika Henzinger, Nitish Korula, Vahab S. Mirrokni, and Clifford Stein. Online stochastic packing applied to display ad allocation. In Mark de Berg and Ulrich Meyer, editors, Algorithms - ESA 2010, 18th Annual European Symposium, Liverpool, UK, September 6-8, 2010. Proceedings, Part I, volume 6346 of Lecture Notes in Computer Science, pages 182-194. Springer, 2010. URL: https://doi.org/10.1007/978-3-642-15775-2_16.
  19. Sreenivas Gollapudi and Debmalya Panigrahi. Online algorithms for rent-or-buy with expert advice. In Kamalika Chaudhuri and Ruslan Salakhutdinov, editors, Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, volume 97 of Proceedings of Machine Learning Research, pages 2319-2327. PMLR, 2019. URL: http://proceedings.mlr.press/v97/gollapudi19a.html.
  20. Anupam Gupta and Marco Molinaro. How the experts algorithm can help solve lps online. Math. Oper. Res., 41(4):1404-1431, 2016. URL: https://doi.org/10.1287/moor.2016.0782.
  21. Rishi Gupta and Tim Roughgarden. A PAC approach to application-specific algorithm selection. SIAM J. Comput., 46(3):992-1017, 2017. URL: https://doi.org/10.1137/15M1050276.
  22. Piotr Indyk, Frederik Mallmann-Trenn, Slobodan Mitrovic, and Ronitt Rubinfeld. Online page migration with ML advice. CoRR, abs/2006.05028, 2020. URL: http://arxiv.org/abs/2006.05028.
  23. Zhihao Jiang, Debmalya Panigrahi, and Kevin Sun. Online algorithms for weighted paging with predictions. In Artur Czumaj, Anuj Dawar, and Emanuela Merelli, editors, 47th International Colloquium on Automata, Languages, and Programming, ICALP 2020, July 8-11, 2020, Saarbrücken, Germany (Virtual Conference), volume 168 of LIPIcs, pages 69:1-69:18. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020. URL: https://doi.org/10.4230/LIPIcs.ICALP.2020.69.
  24. Bala Kalyanasundaram and Kirk Pruhs. An optimal deterministic algorithm for online b-matching. Theor. Comput. Sci., 233(1-2):319-325, 2000. URL: https://doi.org/10.1016/S0304-3975(99)00140-1.
  25. Richard M. Karp, Umesh V. Vazirani, and Vijay V. Vazirani. An optimal algorithm for on-line bipartite matching. In Harriet Ortiz, editor, Proceedings of the 22nd Annual ACM Symposium on Theory of Computing, May 13-17, 1990, Baltimore, Maryland, USA, pages 352-358. ACM, 1990. URL: https://doi.org/10.1145/100216.100262.
  26. Tim Kraska, Alex Beutel, Ed H. Chi, Jeffrey Dean, and Neoklis Polyzotis. The case for learned index structures. In Proceedings of the 2018 International Conference on Management of Data, SIGMOD '18, pages 489-504, New York, NY, USA, 2018. ACM. Google Scholar
  27. Silvio Lattanzi, Thomas Lavastida, Benjamin Moseley, and Sergei Vassilvitskii. Online scheduling via learned weights. In Shuchi Chawla, editor, Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms, SODA 2020, Salt Lake City, UT, USA, January 5-8, 2020, pages 1859-1877. SIAM, 2020. URL: https://doi.org/10.1137/1.9781611975994.114.
  28. Thodoris Lykouris and Sergei Vassilvtiskii. Competitive caching with machine learned advice. In Jennifer Dy and Andreas Krause, editors, Proceedings of the 35th International Conference on Machine Learning, volume 80 of Proceedings of Machine Learning Research, pages 3302-3311, Stockholmsmässan, Stockholm Sweden, July 2018. PMLR. URL: http://proceedings.mlr.press/v80/lykouris18a.html.
  29. Mohammad Mahdian, Hamid Nazerzadeh, and Amin Saberi. Online optimization with uncertain information. ACM Trans. Algorithms, 8(1):2:1-2:29, 2012. URL: https://doi.org/10.1145/2071379.2071381.
  30. Aranyak Mehta, Amin Saberi, Umesh V. Vazirani, and Vijay V. Vazirani. Adwords and generalized online matching. J. ACM, 54(5):22, 2007. URL: https://doi.org/10.1145/1284320.1284321.
  31. Michael Mitzenmacher. A model for learned bloom filters and optimizing by sandwiching. In Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, 3-8 December 2018, Montréal, Canada., pages 462-471, 2018. Google Scholar
  32. Michael Mitzenmacher and Sergei Vassilvitskii. Algorithms with predictions, 2020. URL: http://arxiv.org/abs/2006.09123.
  33. Marco Molinaro and R. Ravi. Geometry of online packing linear programs. In Artur Czumaj, Kurt Mehlhorn, Andrew M. Pitts, and Roger Wattenhofer, editors, Automata, Languages, and Programming - 39th International Colloquium, ICALP 2012, Warwick, UK, July 9-13, 2012, Proceedings, Part I, volume 7391 of Lecture Notes in Computer Science, pages 701-713. Springer, 2012. URL: https://doi.org/10.1007/978-3-642-31594-7_59.
  34. Manish Purohit, Zoya Svitkina, and Ravi Kumar. Improving online algorithms via ML predictions. In Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, 3-8 December 2018, Montréal, Canada., pages 9684-9693, 2018. URL: http://papers.nips.cc/paper/8174-improving-online-algorithms-via-ml-predictions.
  35. Dhruv Rohatgi. Near-optimal bounds for online caching with machine learned advice. In Shuchi Chawla, editor, Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms, SODA 2020, Salt Lake City, UT, USA, January 5-8, 2020, pages 1834-1845. SIAM, 2020. URL: https://doi.org/10.1137/1.9781611975994.112.
  36. Leslie G. Valiant. A theory of the learnable. Commun. ACM, 27(11):1134-1142, 1984. URL: https://doi.org/10.1145/1968.1972.
  37. Alexander Wei. Better and simpler learning-augmented online caching. CoRR, abs/2005.13716, 2020. URL: http://arxiv.org/abs/2005.13716.
  38. Étienne Bamas, Andreas Maggiori, Lars Rohwedder, and Ola Svensson. Learning augmented energy minimization via speed scaling, 2020. URL: http://arxiv.org/abs/2010.11629.
  39. Étienne Bamas, Andreas Maggiori, and Ola Svensson. The primal-dual method for learning augmented algorithms, 2020. URL: http://arxiv.org/abs/2010.11632.
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail