A simple dynamic bandit algorithm for ...
Type de document :
Communication dans un congrès avec actes
Titre :
A simple dynamic bandit algorithm for hyper-parameter tuning
Auteur(s) :
Shang, Xuedong [Auteur]
Sequential Learning [SEQUEL]
Kaufmann, Emilie [Auteur]
Sequential Learning [SEQUEL]
Valko, Michal [Auteur]
Sequential Learning [SEQUEL]
DeepMind [Paris]
Sequential Learning [SEQUEL]
Kaufmann, Emilie [Auteur]

Sequential Learning [SEQUEL]
Valko, Michal [Auteur]

Sequential Learning [SEQUEL]
DeepMind [Paris]
Titre de la manifestation scientifique :
Workshop on Automated Machine Learning at International Conference on Machine Learning
Organisateur(s) de la manifestation scientifique :
AutoML@ICML 2019 - 6th ICML Workshop on Automated Machine Learning
Ville :
Long Beach
Pays :
Etats-Unis d'Amérique
Date de début de la manifestation scientifique :
2019-06-14
Discipline(s) HAL :
Statistiques [stat]/Machine Learning [stat.ML]
Résumé en anglais : [en]
Hyper-parameter tuning is a major part of modern machine learning systems. The tuning itself can be seen as a sequential resource allocation problem. As such, methods for multi-armed bandits have been already applied. In ...
Lire la suite >Hyper-parameter tuning is a major part of modern machine learning systems. The tuning itself can be seen as a sequential resource allocation problem. As such, methods for multi-armed bandits have been already applied. In this paper, we view hyper-parameter optimization as an instance of best-arm identification in infinitely many-armed bandits. We propose D-TTTS, a new adaptive algorithm inspired by Thompson sampling, which dynamically balances between refining the estimate of the quality of hyper-parameter configurations previously explored and adding new hyper-parameter configurations to the pool of candidates. The algorithm is easy to implement and shows competitive performance compared to state-of-the-art algorithms for hyper-parameter tuning.Lire moins >
Lire la suite >Hyper-parameter tuning is a major part of modern machine learning systems. The tuning itself can be seen as a sequential resource allocation problem. As such, methods for multi-armed bandits have been already applied. In this paper, we view hyper-parameter optimization as an instance of best-arm identification in infinitely many-armed bandits. We propose D-TTTS, a new adaptive algorithm inspired by Thompson sampling, which dynamically balances between refining the estimate of the quality of hyper-parameter configurations previously explored and adding new hyper-parameter configurations to the pool of candidates. The algorithm is easy to implement and shows competitive performance compared to state-of-the-art algorithms for hyper-parameter tuning.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
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