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arXiv · 2007.08448

Comparator-adaptive Convex Bandits

Dirk van der Hoeven·Ashok Cutkosky·Haipeng Luo
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Abstract

We study bandit convex optimization methods that adapt to the norm of the comparator, a topic that has only been studied before for its full-information counterpart. Specifically, we develop convex bandit algorithms with regret bounds that are small whenever the norm of the comparator is small. We first use techniques from the full-information setting to develop comparator-adaptive algorithms for linear bandits. Then, we extend the ideas to convex bandits with Lipschitz or smooth loss functions, using a new single-point gradient estimator and carefully designed surrogate losses.

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BibTeXRIS

Dirk van der Hoeven, Ashok Cutkosky, Haipeng Luo. 2020-07-16. Comparator-adaptive Convex Bandits. https://arxiv.org/abs/2007.08448

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