fair-bandits

This paper introduces a discrepancy-rounding framework for stochastic bandits with exact minimum-exposure constraints, achieving fair regret governed by the nonmandatory budget rather than horizon. It proposes algorithms with minimax and instance-dependent optimality guarantees, handles time-varying and overlapping group floors, and validates through experiments.