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Improved PAC-Bayesian Bounds for Linear Regression
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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]
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] refId
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 ...
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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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  • https://hal.inria.fr/hal-02396556/document
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