Top-m identification for linear bandits

Clémence Réda · Emilie Kaufmann · Andrée Delahaye-Duriez

Keywords: [ Learning Theory and Statistics ] [ Decision Processes and Bandits ]


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.

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