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Conference Papers Year : 2021

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 features might decrease the sample complexity. We further validate these algorithms empirically on simulated data and on a simple drug repurposing task.
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Dates and versions

hal-03172145 , version 1 (17-03-2021)

Identifiers

  • HAL Id : hal-03172145 , version 1

Cite

Clémence Réda, Emilie Kaufmann, Andrée Delahaye-Duriez. Top-m identification for linear bandits. Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS), 2021, Virtual, United States. ⟨hal-03172145⟩
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