no code implementations • 10 Jan 2023 • Volkan Cevher, Georgios Piliouras, Ryann Sim, Stratis Skoulakis
In this paper we present a first-order method that admits near-optimal convergence rates for convex/concave min-max problems while requiring a simple and intuitive analysis.
no code implementations • 18 Jul 2022 • Georgios Piliouras, Lillian Ratliff, Ryann Sim, Stratis Skoulakis
The study of learning in games has thus far focused primarily on normal form games.
no code implementations • 29 Nov 2021 • Georgios Piliouras, Ryann Sim, Stratis Skoulakis
This implies that the CMWU dynamics converge with rate $O(nV \log m \log T / T)$ to a \textit{Coarse Correlated Equilibrium}.
no code implementations • NeurIPS 2021 • Tanner Fiez, Ryann Sim, Stratis Skoulakis, Georgios Piliouras, Lillian Ratliff
Classical learning results build on this theorem to show that online no-regret dynamics converge to an equilibrium in a time-average sense in zero-sum games.
1 code implementation • 15 Dec 2020 • Stratis Skoulakis, Tanner Fiez, Ryann Sim, Georgios Piliouras, Lillian Ratliff
The predominant paradigm in evolutionary game theory and more generally online learning in games is based on a clear distinction between a population of dynamic agents that interact given a fixed, static game.
