Author

Weiqiang Zheng

Yale University

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Aug 2026

Separation of Nonergodic Uniform Convergence Rates for Regularized Learning in Games

Convergence of Optimistic Learning in Games and the Role of Forgetfulness Online learning algorithms solve games by repeatedly updating players’ strategies. A natural hope is that the latest strategy improves at a predictable rate. This paper shows that this intuition can fail for optimistic follow-the-regularized-lea...

Yang Cai, Gabriele Farina, J. Grand-Clément et al. · Operational Research · 0 citations

#machine learning Preprint Sep 2026

Algorithmic Collusion and the Complexity of Information-Value-Free Equilibria

The complexity of IVF(C)CEs in succinct games, which model more realistic strategic interactions that feature either many players or exponentially many pure strategies, is examined, showing that it is at least as hard as the P-matrix linear complementarity problem, and hence as hard as simple stochastic games.

I. Anagnostides, Weiqiang Zheng · 1 citation

Preprint Aug 2026

Optimal Alternating Regret for Online Learning and Games

We settle the minimax-optimal alternating regret, a regret notion motivated by alternating learning dynamics in games, for both online linear optimization (OLO) and online convex optimization (OCO). For OLO over the probability simplex $\Delta_d$, we give an algorithm with $O(\log d)$ alternating regret that remains a...

Yixin Tao, Weiqiang Zheng · 2 citations