Titre :

Black-box optimization of noisy functions with unknown smoothness

Auteur(s) :

Grill, Jean-Bastien [Auteur]
Sequential Learning [SEQUEL]
Valko, Michal [Auteur]
Sequential Learning [SEQUEL]
Munos, Rémi [Auteur]
Sequential Learning [SEQUEL]
DeepMind [London]

Titre de la manifestation scientifique :

Neural Information Processing Systems

Ville :

Montréal

Pays :

Canada

Date de début de la manifestation scientifique :

2015

Discipline(s) HAL :

Statistiques [stat]/Machine Learning [stat.ML]
Informatique [cs]/Recherche d'information [cs.IR]

Résumé en anglais : [en]

We study the problem of black-box optimization of a function $f$ of any dimension, given function evaluations perturbed by noise. The function is assumed to be locally smooth around one of its global optima, but this ...
Lire la suite >We study the problem of black-box optimization of a function $f$ of any dimension, given function evaluations perturbed by noise. The function is assumed to be locally smooth around one of its global optima, but this smoothness is unknown. Our contribution is an adaptive optimization algorithm, POO or parallel optimistic optimization, that is able to deal with this setting. POO performs almost as well as the best known algorithms requiring the knowledge of the smoothness. Furthermore, POO works for a larger class of functions than what was previously considered, especially for functions that are difficult to optimize, in a very precise sense. We provide a finite-time analysis of POO's performance, which shows that its error after $n$ evaluations is at most a factor of $\sqrt{\ln n}$ away from the error of the best known optimization algorithms using the knowledge of the smoothness.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Projet ANR :

Extraction et transfert de connaissances dans l'apprentissage par renforcement

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL