Poster
Bandit Smooth Convex Optimization: Improving the Bias-Variance Tradeoff
Ofer Dekel · Ronen Eldan · Tomer Koren
210 C #98
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Abstract
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Abstract:
Bandit convex optimization is one of the fundamental problems in the field of online learning. The best algorithm for the general bandit convex optimization problem guarantees a regret of , while the best known lower bound is . Many attemptshave been made to bridge the huge gap between these bounds. A particularly interesting special case of this problem assumes that the loss functions are smooth. In this case, the best known algorithm guarantees a regret of . We present an efficient algorithm for the banditsmooth convex optimization problem that guarantees a regret of . Our result rules out an lower bound and takes a significant step towards the resolution of this open problem.
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