Better Algorithms for Stochastic Bandits with Adversarial Corruptions

Anupam Gupta; Tomer Koren; Kunal Talwar

Better Algorithms for Stochastic Bandits with Adversarial Corruptions

Anupam Gupta, Tomer Koren, Kunal Talwar

Research output: Contribution to journal › Conference article › peer-review

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.

Original language	English
Pages (from-to)	1562-1578
Number of pages	17
Journal	Proceedings of Machine Learning Research
Volume	99
State	Published - 2019
Externally published	Yes
Event	32nd Conference on Learning Theory, COLT 2019 - Phoenix, United States Duration: 25 Jun 2019 → 28 Jun 2019 https://proceedings.mlr.press/v99

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Software
Control and Systems Engineering
Statistics and Probability

Cite this

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title = "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.",

author = "Anupam Gupta and Tomer Koren and Kunal Talwar",

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T1 - Better Algorithms for Stochastic Bandits with Adversarial Corruptions

AU - Gupta, Anupam

AU - Koren, Tomer

AU - Talwar, Kunal

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N2 - 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.

AB - 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.

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M3 - مقالة من مؤنمر

SN - 2640-3498

VL - 99

SP - 1562

EP - 1578

JO - Proceedings of Machine Learning Research

JF - Proceedings of Machine Learning Research

T2 - 32nd Conference on Learning Theory, COLT 2019

Y2 - 25 June 2019 through 28 June 2019

ER -

Better Algorithms for Stochastic Bandits with Adversarial Corruptions

Abstract

All Science Journal Classification (ASJC) codes

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