Search Results for author: Maxwell Fishelson

Found 4 papers, 0 papers with code

From External to Swap Regret 2.0: An Efficient Reduction and Oblivious Adversary for Large Action Spaces

no code implementations30 Oct 2023 Yuval Dagan, Constantinos Daskalakis, Maxwell Fishelson, Noah Golowich

We provide a novel reduction from swap-regret minimization to external-regret minimization, which improves upon the classical reductions of Blum-Mansour [BM07] and Stolz-Lugosi [SL05] in that it does not require finiteness of the space of actions.

Online Learning and Solving Infinite Games with an ERM Oracle

no code implementations4 Jul 2023 Angelos Assos, Idan Attias, Yuval Dagan, Constantinos Daskalakis, Maxwell Fishelson

In this setting, we provide learning algorithms that only rely on best response oracles and converge to approximate-minimax equilibria in two-player zero-sum games and approximate coarse correlated equilibria in multi-player general-sum games, as long as the game has a bounded fat-threshold dimension.

Binary Classification

Near-Optimal No-Regret Learning for Correlated Equilibria in Multi-Player General-Sum Games

no code implementations11 Nov 2021 Ioannis Anagnostides, Constantinos Daskalakis, Gabriele Farina, Maxwell Fishelson, Noah Golowich, Tuomas Sandholm

Recently, Daskalakis, Fishelson, and Golowich (DFG) (NeurIPS`21) showed that if all agents in a multi-player general-sum normal-form game employ Optimistic Multiplicative Weights Update (OMWU), the external regret of every player is $O(\textrm{polylog}(T))$ after $T$ repetitions of the game.

Near-Optimal No-Regret Learning in General Games

no code implementations NeurIPS 2021 Constantinos Daskalakis, Maxwell Fishelson, Noah Golowich

We show that Optimistic Hedge -- a common variant of multiplicative-weights-updates with recency bias -- attains ${\rm poly}(\log T)$ regret in multi-player general-sum games.

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