# Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions

@article{He2022NearlyOA, title={Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions}, author={Jiafan He and Dongruo Zhou and Tong Zhang and Quanquan Gu}, journal={ArXiv}, year={2022}, volume={abs/2205.06811} }

We study the linear contextual bandit problem in the presence of adversarial corruption, where the reward at each round is corrupted by an adversary, and the corruption level (i.e., the sum of corruption magnitudes over the horizon) is C ≥ 0. The best-known algorithms in this setting are limited in that they either are computationally ineﬃcient or require a strong assumption on the corruption, or their regret is at least C times worse than the regret without corruption. In this paper, to…

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