Top-m identification for linear bandits - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2021

Top-m identification for linear 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 features might decrease the sample complexity. We further validate these algorithms empirically on simulated data and on a simple drug repurposing task.
Fichier principal
Vignette du fichier
reda2021top.pdf (2.28 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

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


  • HAL Id : hal-03172145 , version 1


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⟩
91 View
77 Download


Gmail Facebook X LinkedIn More