6 papers · 1 filter
Is Online Linear Optimization Sufficient for Strategic Robustness?
Yang Cai, Haipeng Luo, Chen-Yu Wei +1
We consider bidding in repeated Bayesian first-price auctions. Bidding algorithms that achieve optimal regret have been extensively studied, but their strategic robustness to the s…
Proximal Regret and Proximal Correlated Equilibria: A New Tractable Solution Concept for Online Learning and Games
Yang Cai, Constantinos Daskalakis, Haipeng Luo +2
Learning and computation of equilibria are central problems in game theory, theory of computation, and artificial intelligence. In this work, we introduce proximal regret, a new no…
From Average-Iterate to Last-Iterate Convergence in Games: A Reduction and Its Applications
Yang Cai, Haipeng Luo, Chen-Yu Wei +1
The convergence of online learning algorithms in games under self-play is a fundamental question in game theory and machine learning. Among various notions of convergence, last-ite…
Fast Last-Iterate Convergence of Learning in Games Requires Forgetful Algorithms
Yang Cai, Gabriele Farina, Julien Grand-Clément +4
Self-play via online learning is one of the premier ways to solve large-scale two-player zero-sum games, both in theory and practice. Particularly popular algorithms include optimi…
On Tractable -Equilibria in Non-Concave Games
Yang Cai, Constantinos Daskalakis, Haipeng Luo +2
While Online Gradient Descent and other no-regret learning procedures are known to efficiently converge to a coarse correlated equilibrium in games where each agent's utility is co…
Last-Iterate Convergence Properties of Regret-Matching Algorithms in Games
Yang Cai, Gabriele Farina, Julien Grand-Clément +4
We study last-iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching (RM). Despite their widespread use for solving rea…