arxivstat.MLcs.AIcs.LG2026-07-15
Price of Fairness in Bandits: A Tight Minimax Characterization
Dhruv Sarkar, Soumyadeep Dutta, Sayak Ray Chowdhury
In bandit problems, standard regret-minimizing algorithms treat exploration as an amortized cost, which can expose early participants to unfair ex-ante losses in settings such as clinical trials. Recent work addresses this by evaluating the sequence of per-round expected rewards…