Gamification of pure exploration for linear bandits

Degenne, Rémy; Ménard, Pierre; Shang, Xuedong; Valko, Michal

Document type :

Communication dans un congrès avec actes

Title :

Gamification of pure exploration for linear bandits

Author(s) :

Degenne, Rémy [Auteur]
Statistical Machine Learning and Parsimony [SIERRA]
Ménard, Pierre [Auteur]
Scool [Scool]
Sequential Learning [SEQUEL]
Shang, Xuedong [Auteur]
Scool [Scool]
Sequential Learning [SEQUEL]
Valko, Michal [Auteur]

DeepMind [Paris]

Conference title :

ICML 2020 - International Conference on Machine Learning

City :

Vienna / Virtual

Country :

Autriche

Start date of the conference :

2020-08-12

HAL domain(s) :

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

English abstract : [en]

We investigate an active pure-exploration setting, that includes best-arm identification, in the context of linear stochastic bandits. While asymptotically optimal algorithms exist for standard multi-arm bandits, the ...
Show more >We investigate an active pure-exploration setting, that includes best-arm identification, in the context of linear stochastic bandits. While asymptotically optimal algorithms exist for standard multi-arm bandits, the existence of such algorithms for the best-arm identification in linear bandits has been elusive despite several attempts to address it. First, we provide a thorough comparison and new insight over different notions of optimality in the linear case, including G-optimality, transductive optimality from optimal experimental designand asymptotic optimality. Second, we design the first asymptotically optimal algorithm for fixed-confidence pure exploration in linear bandits. As a consequence, our algorithm naturally bypasses the pitfall caused by a simple but difficult instance, that most prior algorithms had to be engineered to deal with explicitly. Finally, we avoid the need to fully solve an optimal design problem by providing an approach that entails an efficient implementation.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
PaRis Artificial Intelligence Research InstitutE

Comment :

Virtual conference