Top-M Identification for Linear Bandits
Abstract
Motivated by an application to drug repurposing, we propose the first algorithms to tackle the identification of the m ≥ 1 arms with largest means in a linear bandit model, in the fixed-confidence setting. These algorithms belong to the generic family of Gap-Index Focused Algorithms (GIFA) that we introduce for Top-m identification in linear bandits. We propose a unified analysis of these algorithms, which shows how the use of contexts might decrease the sample complexity. We further validate these algorithms empirically on simulated data and on a simple drug repurposing task.
Cite
Text
Réda et al. "Top-M Identification for Linear Bandits." Artificial Intelligence and Statistics, 2021.Markdown
[Réda et al. "Top-M Identification for Linear Bandits." Artificial Intelligence and Statistics, 2021.](https://mlanthology.org/aistats/2021/reda2021aistats-topm/)BibTeX
@inproceedings{reda2021aistats-topm,
title = {{Top-M Identification for Linear Bandits}},
author = {Réda, Clémence and Kaufmann, Emilie and Delahaye-Duriez, Andrée},
booktitle = {Artificial Intelligence and Statistics},
year = {2021},
pages = {1108-1116},
volume = {130},
url = {https://mlanthology.org/aistats/2021/reda2021aistats-topm/}
}