Improved PAC-Bayesian Bounds for Linear Regression

Shalaeva, Vera; Fakhrizadeh Esfahani, Alireza; Germain, Pascal; Petreczky, Mihaly

Document type :

Communication dans un congrès avec actes

Title :

Improved PAC-Bayesian Bounds for Linear Regression

Author(s) :

Shalaeva, Vera [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Fakhrizadeh Esfahani, Alireza [Auteur]
Université de Lille
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Germain, Pascal [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Petreczky, Mihaly [Auteur]

Université de Lille
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]

Conference title :

AAAI 2020 - Thirty-Fourth AAAI Conference on Artificial Intelligence

City :

New York

Country :

Etats-Unis d'Amérique

Start date of the conference :

2020-02-07

HAL domain(s) :

Informatique [cs]/Apprentissage [cs.LG]

English abstract : [en]

In this paper, we improve the PAC-Bayesian error bound for linear regression derived in Germain et al. [10]. The improvements are twofold. First, the proposed error bound is tighter, and converges to the generalization ...
Show more >In this paper, we improve the PAC-Bayesian error bound for linear regression derived in Germain et al. [10]. The improvements are twofold. First, the proposed error bound is tighter, and converges to the generalization loss with a well-chosen temperature parameter. Second, the error bound also holds for training data that are not independently sampled. In particular, the error bound applies to certain time series generated by well-known classes of dynamical models, such as ARX models.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

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

Source :

Harvested from HAL

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http://arxiv.org/pdf/1912.03036
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1912.03036
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Improved PAC-Bayesian Bounds for Linear Regression

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