Better Algorithms for Stochastic Bandits with Adversarial Corruptions

Anupam Gupta, Tomer Koren, Kunal Talwar
Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1562-1578, 2019.

Abstract

We study the stochastic multi-armed bandits problem in the presence of adversarial corruption. We present a new algorithm for this problem whose regret is nearly optimal, substantially improving upon previous work. Our algorithm is agnostic to the level of adversarial contamination and can tolerate a significant amount of corruption with virtually no degradation in performance.

Cite this Paper

BibTeX
@InProceedings{pmlr-v99-gupta19a, title = {Better Algorithms for Stochastic Bandits with Adversarial Corruptions}, author = {Gupta, Anupam and Koren, Tomer and Talwar, Kunal}, booktitle = {Proceedings of the Thirty-Second Conference on Learning Theory}, pages = {1562--1578}, year = {2019}, editor = {Beygelzimer, Alina and Hsu, Daniel}, volume = {99}, series = {Proceedings of Machine Learning Research}, month = {25--28 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v99/gupta19a/gupta19a.pdf}, url = {https://proceedings.mlr.press/v99/gupta19a.html}, abstract = {We study the stochastic multi-armed bandits problem in the presence of adversarial corruption. We present a new algorithm for this problem whose regret is nearly optimal, substantially improving upon previous work. Our algorithm is agnostic to the level of adversarial contamination and can tolerate a significant amount of corruption with virtually no degradation in performance.} }
Endnote
%0 Conference Paper %T Better Algorithms for Stochastic Bandits with Adversarial Corruptions %A Anupam Gupta %A Tomer Koren %A Kunal Talwar %B Proceedings of the Thirty-Second Conference on Learning Theory %C Proceedings of Machine Learning Research %D 2019 %E Alina Beygelzimer %E Daniel Hsu %F pmlr-v99-gupta19a %I PMLR %P 1562--1578 %U https://proceedings.mlr.press/v99/gupta19a.html %V 99 %X We study the stochastic multi-armed bandits problem in the presence of adversarial corruption. We present a new algorithm for this problem whose regret is nearly optimal, substantially improving upon previous work. Our algorithm is agnostic to the level of adversarial contamination and can tolerate a significant amount of corruption with virtually no degradation in performance.
APA
Gupta, A., Koren, T. & Talwar, K.. (2019). Better Algorithms for Stochastic Bandits with Adversarial Corruptions. Proceedings of the Thirty-Second Conference on Learning Theory, in Proceedings of Machine Learning Research 99:1562-1578 Available from https://proceedings.mlr.press/v99/gupta19a.html.

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