Top-m identification for linear bandits

• URL: http://arxiv.org/abs/2103.10070v1
• Date: Thu, 18 Mar 2021 08:04:45 GMT
• Title: Top-m identification for linear bandits
• Authors: Cl\'emence R\'eda (UP M\'edecine Paris Nord, INSERM), Emilie Kaufmann (CNRS, Lille DECCID SID), Andr\'ee Delahaye-Duriez (UP M\'edecine Paris Nord, INSERM)
• Abstract summary: Motivated by an application to drug repurposing, we propose the first algorithms to tackle the identification of the m $ge$ 1 arms with largest means in a linear bandit model. These algorithms belong to the generic family of Gap-Index Focused Algorithms (GIFA) that we introduce for Top-m identification in linear bandits.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Motivated by an application to drug repurposing, we propose the first algorithms to tackle the identification of the m $\ge$ 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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