AI RESEARCH
Achieving Better Local Regret Bound for Online Non-Convex Bilevel Optimization
arXiv CS.LG
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ArXi:2602.06457v2 Announce Type: replace Online bilevel optimization (OBO) has emerged as a powerful framework for many machine learning problems. Prior works have developed several algorithms that minimize the standard bilevel local regret or the window-averaged bilevel local regret of the OBO problem, but the optimality of existing regret bounds remains unclear. In this work, we establish optimal regret bounds for both settings.