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  • title: Near-Optimal No-Regret Learning Dynamics for General Convex Games
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            Near-Optimal No-Regret Learning Dynamics for General Convex Games
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            Near-Optimal No-Regret Learning Dynamics for General Convex Games

            Nov 28, 2022

            Speakers

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            Gabriele Farina

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            IA

            Ioannis Anagnostides

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            HL

            Haipeng Luo

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            About

            A recent line of work has established uncoupled learning dynamics such that, when employed by all players in a game, each player's regret after T repetitions grows polylogarithmically in T, an exponential improvement over the traditional guarantees within the no-regret framework. However, so far these results have only been limited to certain classes of games with structured strategy spaces—such as normal-form and extensive-form games. The question as to whether O(polylog T) regret bounds can be…

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            NeurIPS 2022

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