Gap Dependent

Gap-dependent analysis in online learning focuses on improving the efficiency of algorithms by exploiting differences in the quality of choices (gaps). Current research investigates this in various settings, including multi-armed bandits, matrix completion, and reinforcement learning, employing algorithms like UCB variants, phased elimination methods, and policy optimization techniques with entropy regularization. This research aims to achieve tighter regret bounds, moving beyond worst-case scenarios to more optimistic estimates of performance, ultimately leading to more efficient and effective algorithms for a wide range of applications. The resulting improvements in algorithm efficiency have significant implications for resource allocation, recommendation systems, and decision-making under uncertainty.