Gamification of pure exploration for linear ...
Type de document :
Communication dans un congrès avec actes
Titre :
Gamification of pure exploration for linear bandits
Auteur(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]
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]
Titre de la manifestation scientifique :
ICML 2020 - International Conference on Machine Learning
Ville :
Vienna / Virtual
Pays :
Autriche
Date de début de la manifestation scientifique :
2020-08-12
Discipline(s) HAL :
Statistiques [stat]/Machine Learning [stat.ML]
Résumé en anglais : [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 ...
Lire la suite >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.Lire moins >
Lire la suite >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.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Projet ANR :
Commentaire :
Virtual conference
Collections :
Source :
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