1 citations · 1 across the 7 of their papers we have counts for
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Fast and Furious Symmetric Learning in Zero-Sum Games: Gradient Descent as Fictitious Play
John Lazarsfeld, Georgios Piliouras, Ryann Sim +1
This paper investigates the sublinear regret guarantees of two non-no-regret algorithms in zero-sum games: Fictitious Play, and Online Gradient Descent with constant stepsizes. In…
Online Learning in Stabilized Linear Dynamical Games with Adversarial Disturbances
Anas Barakat, John Lazarsfeld, Georgios Piliouras +1
We study online learning in linear dynamical games where multiple strategic agents act on a shared state evolving according to a linear dynamical system subject to adversarial dist…
Optimism Without Regularization: Constant Regret in Zero-Sum Games
John Lazarsfeld, Georgios Piliouras, Ryann Sim +1
This paper studies the optimistic variant of Fictitious Play for learning in two-player zero-sum games. While it is known that Optimistic FTRL -- a regularized algorithm with a bou…
Simple Opinion Dynamics for No-Regret Learning
John Lazarsfeld, Dan Alistarh
We study a cooperative multi-agent bandit setting in the distributed GOSSIP model: in every round, each of agents chooses an action from a common set, observes the action's cor…