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arXiv · 2007.08703

Bandits for BMO Functions

Tianyu Wang·Cynthia Rudin
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Abstract

We study the bandit problem where the underlying expected reward is a Bounded Mean Oscillation (BMO) function. BMO functions are allowed to be discontinuous and unbounded, and are useful in modeling signals with infinities in the do-main. We develop a toolset for BMO bandits, and provide an algorithm that can achieve poly-log $\delta$-regret -- a regret measured against an arm that is optimal after removing a $\delta$-sized portion of the arm space.

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Tianyu Wang, Cynthia Rudin. 2020-07-17. Bandits for BMO Functions. https://arxiv.org/abs/2007.08703

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