Pliable rejection sampling
Type de document :
Communication dans un congrès avec actes
Titre :
Pliable rejection sampling
Auteur(s) :
Erraqabi, Akram [Auteur]
Université de Montréal [UdeM]
Sequential Learning [SEQUEL]
Valko, Michal [Auteur]
Sequential Learning [SEQUEL]
Carpentier, Alexandra [Auteur]
Institut für Mathematik [Potsdam]
Maillard, Odalric-Ambrym [Auteur]
Machine Learning and Optimisation [TAO]
Université de Montréal [UdeM]
Sequential Learning [SEQUEL]
Valko, Michal [Auteur]
Sequential Learning [SEQUEL]
Carpentier, Alexandra [Auteur]
Institut für Mathematik [Potsdam]
Maillard, Odalric-Ambrym [Auteur]
Machine Learning and Optimisation [TAO]
Titre de la manifestation scientifique :
International Conference on Machine Learning
Ville :
New York City
Pays :
Etats-Unis d'Amérique
Date de début de la manifestation scientifique :
2016-06-19
Discipline(s) HAL :
Statistiques [stat]/Machine Learning [stat.ML]
Résumé en anglais : [en]
Rejection sampling is a technique for sampling from difficult distributions. However, its use is limited due to a high rejection rate. Common adaptive rejection sampling methods either work only for very specific distributions ...
Lire la suite >Rejection sampling is a technique for sampling from difficult distributions. However, its use is limited due to a high rejection rate. Common adaptive rejection sampling methods either work only for very specific distributions or without performance guarantees. In this paper, we present pliable rejection sampling (PRS), a new approach to rejection sampling, where we learn the sampling proposal using a kernel estimator. Since our method builds on rejection sampling, the samples obtained are with high probability i.i.d. and distributed according to f. Moreover, PRS comes with a guarantee on the number of accepted samples.Lire moins >
Lire la suite >Rejection sampling is a technique for sampling from difficult distributions. However, its use is limited due to a high rejection rate. Common adaptive rejection sampling methods either work only for very specific distributions or without performance guarantees. In this paper, we present pliable rejection sampling (PRS), a new approach to rejection sampling, where we learn the sampling proposal using a kernel estimator. Since our method builds on rejection sampling, the samples obtained are with high probability i.i.d. and distributed according to f. Moreover, PRS comes with a guarantee on the number of accepted samples.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
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