On Elimination Strategies for Bandit Fixed-Confidence Identification

Tirinzoni, Andrea; Degenne, Rémy

Type de document :

Communication dans un congrès avec actes

Titre :

On Elimination Strategies for Bandit Fixed-Confidence Identification

Auteur(s) :

Tirinzoni, Andrea [Auteur]
Meta AI
Degenne, Rémy [Auteur]
Scool [Scool]

Titre de la manifestation scientifique :

NeurIPS 2022 - 36th Conference on Neural Information Processing System

Ville :

New Orleans

Pays :

Etats-Unis d'Amérique

Date de début de la manifestation scientifique :

2022-11-28

Discipline(s) HAL :

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

Résumé en anglais : [en]

Elimination algorithms for bandit identification, which prune the plausible correct answers sequentially until only one remains, are computationally convenient since they reduce the problem size over time. However, existing ...
Lire la suite >Elimination algorithms for bandit identification, which prune the plausible correct answers sequentially until only one remains, are computationally convenient since they reduce the problem size over time. However, existing elimination strategies are often not fully adaptive (they update their sampling rule infrequently) and are not easy to extend to combinatorial settings, where the set of answers is exponentially large in the problem dimension. On the other hand, most existing fully-adaptive strategies to tackle general identification problems are computationally demanding since they repeatedly test the correctness of every answer, without ever reducing the problem size. We show that adaptive methods can be modified to use elimination in both their stopping and sampling rules, hence obtaining the best of these two worlds: the algorithms (1) remain fully adaptive, (2) suffer a sample complexity that is never worse of their non-elimination counterpart, and (3) provably eliminate certain wrong answers early. We confirm these benefits experimentally, where elimination improves significantly the computational complexity of adaptive methods on common tasks like best-arm identification in linear bandits.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :