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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…