Bandits for BMO Functions
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.
Cite
Text
Wang and Rudin. "Bandits for BMO Functions." International Conference on Machine Learning, 2020.Markdown
[Wang and Rudin. "Bandits for BMO Functions." International Conference on Machine Learning, 2020.](https://mlanthology.org/icml/2020/wang2020icml-bandits/)BibTeX
@inproceedings{wang2020icml-bandits,
title = {{Bandits for BMO Functions}},
author = {Wang, Tianyu and Rudin, Cynthia},
booktitle = {International Conference on Machine Learning},
year = {2020},
pages = {9996-10006},
volume = {119},
url = {https://mlanthology.org/icml/2020/wang2020icml-bandits/}
}