Top-m identification for linear bandits

Code

• github.com/clreda/linear-top-m
  OfficialIn papernone★ 4

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.

Reproductions