4 papers
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…
On Separation Between Best-Iterate, Random-Iterate, and Last-Iterate Convergence of Learning in Games
Yang Cai, Gabriele Farina, Julien Grand-Clément +4
Non-ergodic convergence of learning dynamics in games is widely studied recently because of its importance in both theory and practice. Recent work (Cai et al., 2024) showed that a…