ICML 2020poster6 citations
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.
BibTeX
@InProceedings{pmlr-v119-wang20q,
title = {Bandits for {BMO} Functions},
author = {Wang, Tianyu and Rudin, Cynthia},
booktitle = {Proceedings of the 37th International Conference on Machine Learning},
pages = {9996--10006},
year = {2020},
editor = {III, Hal Daumé and Singh, Aarti},
volume = {119},
series = {Proceedings of Machine Learning Research},
month = {13--18 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v119/wang20q/wang20q.pdf},
url = {https://proceedings.mlr.press/v119/wang20q.html},
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.}
}