Top Two Algorithms Revisited

Jourdan, Marc; Degenne, Rémy; Baudry, Dorian; de Heide, Rianne; Kaufmann, Emilie

Document type :

Communication dans un congrès avec actes

Title :

Top Two Algorithms Revisited

Author(s) :

Jourdan, Marc [Auteur]
Scool [Scool]
Degenne, Rémy [Auteur]
Scool [Scool]
Baudry, Dorian [Auteur]
Scool [Scool]

de Heide, Rianne [Auteur]
Vrije Universiteit Amsterdam [Amsterdam] [VU]
Kaufmann, Emilie [Auteur]

Scool [Scool]

Conference title :

NeurIPS 2022 - 36th Conference on Neural Information Processing System

City :

New Orleans

Country :

Etats-Unis d'Amérique

Start date of the conference :

2022-11-28

Journal title :

Advances in Neural Information Processing Systems

HAL domain(s) :

Statistiques [stat]/Machine Learning [stat.ML]

English abstract : [en]

Top Two algorithms arose as an adaptation of Thompson sampling to best arm identification in multi-armed bandit models [38], for parametric families of arms. They select the next arm to sample from by randomizing among two ...
Show more >Top Two algorithms arose as an adaptation of Thompson sampling to best arm identification in multi-armed bandit models [38], for parametric families of arms. They select the next arm to sample from by randomizing among two candidate arms, a leader and a challenger. Despite their good empirical performance, theoretical guarantees for fixed-confidence best arm identification have only been obtained when the arms are Gaussian with known variances. In this paper, we provide a general analysis of Top Two methods, which identifies desirable properties of the leader, the challenger, and the (possibly non-parametric) distributions of the arms. As a result, we obtain theoretically supported Top Two algorithms for best arm identification with bounded distributions. Our proof method demonstrates in particular that the sampling step used to select the leader inherited from Thompson sampling can be replaced by other choices, like selecting the empirical best arm.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

ANR Project :

Au delà de l'apprentissage séquentiel pour de meilleures prises de décisions

Collections :