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Conference Papers Year : 2020

Improved PAC-Bayesian Bounds for Linear Regression


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.
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hal-02396556 , version 1 (06-12-2019)



Vera Shalaeva, Alireza Fakhrizadeh Esfahani, Pascal Germain, Mihaly Petreczky. Improved PAC-Bayesian Bounds for Linear Regression. AAAI 2020 - Thirty-Fourth AAAI Conference on Artificial Intelligence, Feb 2020, New York, United States. ⟨hal-02396556⟩
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