Better Algorithms for Stochastic Bandits with Adversarial Corruptions
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
Text
Gupta et al. "Better Algorithms for Stochastic Bandits with Adversarial Corruptions." Conference on Learning Theory, 2019.Markdown
[Gupta et al. "Better Algorithms for Stochastic Bandits with Adversarial Corruptions." Conference on Learning Theory, 2019.](https://mlanthology.org/colt/2019/gupta2019colt-better/)BibTeX
@inproceedings{gupta2019colt-better,
title = {{Better Algorithms for Stochastic Bandits with Adversarial Corruptions}},
author = {Gupta, Anupam and Koren, Tomer and Talwar, Kunal},
booktitle = {Conference on Learning Theory},
year = {2019},
pages = {1562-1578},
volume = {99},
url = {https://mlanthology.org/colt/2019/gupta2019colt-better/}
}