Titre :

Online PAC-Bayes Learning

Auteur(s) :

Haddouche, Maxime [Auteur]
Guedj, Benjamin [Auteur]
University College of London [London] [UCL]
Department of Computer science [University College of London] [UCL-CS]
Institut National de Recherche en Informatique et en Automatique [Inria]
Inria Lille - Nord Europe
The Alan Turing Institute
The Inria London Programme [Inria-London]
MOdel for Data Analysis and Learning [MODAL]

Discipline(s) HAL :

Informatique [cs]/Apprentissage [cs.LG]
Statistiques [stat]/Machine Learning [stat.ML]
Statistiques [stat]/Théorie [stat.TH]

Résumé en anglais : [en]

Most PAC-Bayesian bounds hold in the batch learning setting where data is collected at once, prior to inference or prediction. This somewhat departs from many contemporary learning problems where data streams are collected ...
Lire la suite >Most PAC-Bayesian bounds hold in the batch learning setting where data is collected at once, prior to inference or prediction. This somewhat departs from many contemporary learning problems where data streams are collected and the algorithms must dynamically adjust. We prove new PAC-Bayesian bounds in this online learning framework, leveraging an updated definition of regret, and we revisit classical PAC-Bayesian results with a batch-to-online conversion, extending their remit to the case of dependent data. Our results hold for bounded losses, potentially \emph{non-convex}, paving the way to promising developments in online learning.Lire moins >

Langue :

Anglais

Commentaire :

20 pages

Collections :

• Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL