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Bandits for BMO Functions

07/17/2020

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by Tianyu Wang, et al.

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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 δ-regret – a regret measured against an arm that is optimal after removing a δ-sized portion of the arm space.

Tianyu Wang
54 publications
Cynthia Rudin
92 publications

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