AI RESEARCH
Optimal Gap-Dependent Regret for Private Stochastic Decision-Theoretic Online Learning
arXiv CS.LG
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ArXi:2605.29148v1 Announce Type: new We study stochastic decision-theoretic online learning with full information and event-level pure differential privacy. A COLT open problem of Hu and Mehta asks to determine the optimal gap-dependent regret rate for stochastic decision-theoretic online learning under pure event-level differential privacy.