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  • title: No-Regret Learning in Games with Noisy Feedback: Faster Rates and Adaptivity via Learning Rate Separation
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            No-Regret Learning in Games with Noisy Feedback: Faster Rates and Adaptivity via Learning Rate Separation
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            No-Regret Learning in Games with Noisy Feedback: Faster Rates and Adaptivity via Learning Rate Separation

            Nov 28, 2022

            Speakers

            YH

            Yu-Guan Hsieh

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            KA

            Kimon Antonakopoulos

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            VC

            Volkan Cevher

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            About

            We examine the problem of regret minimization when the learner is involved in a continuous game with other optimizing agents: in this case, if all players follow a no-regret algorithm, it is possible to achieve significantly lower regret relative to fully adversarial environments. We study this problem in the context of variationally stable games (a class of continuous games which includes all convex-concave and monotone games), and when the players only have access to noisy estimates of their i…

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            NeurIPS 2022

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