Optimal Algorithms for Online Convex Optimization with Multi-Point Bandit Feedback.
Annual Conference Computational Learning Theory2010-06-01 408 citations20 references
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TL;DRThe multi-point bandit setting, in which the player can query each loss function at multiple points, is introduced, and regret bounds that closely resemble bounds for the full information case are proved. (Semantic Scholar)