Bandits for BMO Functions

Tianyu Wang · Cynthia Rudin

Keywords: [ Reinforcement Learning ] [ Online Learning / Bandits ] [ Reinforcement Learning Theory ] [ Online Learning, Active Learning, and Bandits ]

[ Abstract ] [ Join Zoom
Please do not share or post zoom links

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 singularities in the domain. 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.

Chat is not available.