A Primer on PAC-Bayesian Learning
Type de document :
Pré-publication ou Document de travail
Titre :
A Primer on PAC-Bayesian Learning
Auteur(s) :
Guedj, Benjamin [Auteur]
MOdel for Data Analysis and Learning [MODAL]
University College of London [London] [UCL]
Department of Computer science [University College of London] [UCL-CS]
Inria-CWI [Inria-CWI]
The Inria London Programme [Inria-London]
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MOdel for Data Analysis and Learning [MODAL]
University College of London [London] [UCL]
Department of Computer science [University College of London] [UCL-CS]
Inria-CWI [Inria-CWI]
The Inria London Programme [Inria-London]
Discipline(s) HAL :
Statistiques [stat]/Machine Learning [stat.ML]
Informatique [cs]/Apprentissage [cs.LG]
Statistiques [stat]/Théorie [stat.TH]
Informatique [cs]/Apprentissage [cs.LG]
Statistiques [stat]/Théorie [stat.TH]
Résumé en anglais : [en]
Generalised Bayesian learning algorithms are increasingly popular in machine learning, due to their PAC generalisation properties and flexibility. The present paper aims at providing a self-contained survey on the resulting ...
Lire la suite >Generalised Bayesian learning algorithms are increasingly popular in machine learning, due to their PAC generalisation properties and flexibility. The present paper aims at providing a self-contained survey on the resulting PAC-Bayes framework and some of its main theoretical and algorithmic developments.Lire moins >
Lire la suite >Generalised Bayesian learning algorithms are increasingly popular in machine learning, due to their PAC generalisation properties and flexibility. The present paper aims at providing a self-contained survey on the resulting PAC-Bayes framework and some of its main theoretical and algorithmic developments.Lire moins >
Langue :
Anglais
Projet ANR :
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