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Online Learning on Hidden-Convex Losses via Algorithmic Equivalence: Optimal Regret, Geometric Barrier, and Bandit Feedback
Online gradient descent achieves optimal O(sqrt(T)) regret for hidden-convex losses via sharper discrete equivalence, with a necessary Hessian compatibility condition and O(T^{3/4}) bandit regret.
Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026
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