Top-m identification for linear bandits

Clémence Réda, Emilie Kaufmann, Andrée Delahaye-Duriez
Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:1108-1116, 2021.

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 this Paper

BibTeX
@InProceedings{pmlr-v130-reda21a, title = { Top-m identification for linear bandits }, author = {R{\'e}da, Cl{\'e}mence and Kaufmann, Emilie and Delahaye-Duriez, Andr{\'e}e}, booktitle = {Proceedings of The 24th International Conference on Artificial Intelligence and Statistics}, pages = {1108--1116}, year = {2021}, editor = {Banerjee, Arindam and Fukumizu, Kenji}, volume = {130}, series = {Proceedings of Machine Learning Research}, month = {13--15 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v130/reda21a/reda21a.pdf}, url = {https://proceedings.mlr.press/v130/reda21a.html}, 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. } }
Endnote
%0 Conference Paper %T Top-m identification for linear bandits %A Clémence Réda %A Emilie Kaufmann %A Andrée Delahaye-Duriez %B Proceedings of The 24th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2021 %E Arindam Banerjee %E Kenji Fukumizu %F pmlr-v130-reda21a %I PMLR %P 1108--1116 %U https://proceedings.mlr.press/v130/reda21a.html %V 130 %X 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.
APA
Réda, C., Kaufmann, E. & Delahaye-Duriez, A.. (2021). Top-m identification for linear bandits . Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 130:1108-1116 Available from https://proceedings.mlr.press/v130/reda21a.html.

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