3 papers
math.OC2026
Accelerating Min-Max Optimization via Power-Law Stepsizes
Yue Wu, Weiqiang Zheng, Yang Cai +1
We revisit the convergence guarantees of the Extragradient (EG) method for unconstrained biaffine min-max optimization. It is known that EG with a fixed stepsize achieves a $Î(T^{…
cs.LG2026
Adversarial Learning in Games with Bandit Feedback: Logarithmic Pure-Strategy Maximin Regret
Shinji Ito, Haipeng Luo, Arnab Maiti +2
Learning to play zero-sum games is a fundamental problem in game theory and machine learning. While significant progress has been made in minimizing external regret in the self-pla…
cs.LG2025
Instance-Dependent Regret Bounds for Learning Two-Player Zero-Sum Games with Bandit Feedback
Shinji Ito, Haipeng Luo, Taira Tsuchiya +1
No-regret self-play learning dynamics have become one of the premier ways to solve large-scale games in practice. Accelerating their convergence via improving the regret of the pla…