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Sharp Oracle-Regret Tradeoffs for Projection-Free Online Convex Optimization
We characterize the regret attainable in online convex optimization when access to the feasible set is limited to an exact linear optimization oracle. The learner is given an inscribed ball and a diameter bound and must remain feasible on every consistent instance. For convex $G$-Lipschitz losses, d
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