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This paper introduces Repeated Policy Regret (RP-Regret), a game-theoretic metric for regret minimization in repeated games with adaptive opponents, and proposes three algorithms to minimize it, showing that doing so can lead to cooperative equilibria like in Stag-Hunt.
GLENS is a data-efficient global search method that uses diffusion models to generate diverse, high-quality initial guesses for local minima in non-convex optimization problems by leveraging intermediate solver iterates as free data augmentation.
This paper establishes the first population risk bounds for Kolmogorov-Arnold Networks trained with mini-batch SGD and DP-SGD using correlated noise, advancing theoretical understanding of KANs in privacy-sensitive domains.