AI RESEARCH
Optimal High-Probability Regret for Online Convex Optimization with Two-Point Bandit Feedback
arXiv CS.LG
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ArXi:2603.25029v1 Announce Type: new We consider the problem of Online Convex Optimization (OCO) with two-point bandit feedback in an adversarial environment. In this setting, a player attempts to minimize a sequence of adversarially generated convex loss functions, while only observing the value of each function at two points. While it is well-known that two-point feedback allows for gradient estimation, achieving tight high-probability regret bounds for strongly convex functions still remained open as highlighted by \citet{agarwal2010optimal.