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20202026
most citedNash Convergence of Mean-Based Learning Algorithms in First-Price Auctions

10 citations · 14 across the 23 of their papers we have counts for

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5 papers · 1 filter

cs.GT2025

Fisher Meets Lindahl: A Unified Duality Framework for Market Equilibrium

Yixin Tao, Weiqiang Zheng

The Fisher market equilibrium for private goods and the Lindahl equilibrium for public goods are classic and fundamental solution concepts for market equilibria. While Fisher marke…

cs.GT2025

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…

cs.GT2025

From Best Responses to Learning: Investment Efficiency in Dynamic Environment

Ce Li, Qianfan Zhang, Weiqiang Zheng

We study the welfare of a mechanism in a dynamic environment where a learning investor can make a costly investment to change her value. In many real-world problems, the common ass…

cs.GT2025

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…

cs.LG2025

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…