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Nearly Minimax Optimal Regret for Adversarial Linear Bandits with Strong Corruptions
We study adversarial linear bandits with strong corruption, in which an adaptive adversary chooses the reward vectors and a strong adversary corrupts each observed reward after seeing the learner's action, subject to a known total corruption budget $C$. Strong corruption breaks the importance-weight
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