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MiS Preprint

PAC-Bayes and Information Complexity

Pradeep Kumar Banerjee and Guido Montúfar


We point out that a number of well-known PAC-Bayesian-style and information-theoretic generalization bounds for randomized learning algorithms can be derived under a common framework starting from a fundamental information exponential inequality. We also obtain new bounds for data-dependent priors and unbounded loss functions. Optimizing these bounds naturally gives rise to a method called Information Complexity Minimization for which we discuss two practical examples for learning with neural networks, namely Entropy- and PAC-Bayes- SGD.

Sep 20, 2021
Sep 20, 2021
MSC Codes:
68Q32, 68T05, 94A15
PAC-Bayes generalization bounds, Gibbs algorithm, flat minima

Related publications

2021 Repository Open Access
Pradeep Kumar Banerjee and Guido Montúfar

PAC-bayes and information complexity

In: ICLR 2021 workshop on neural compression : from information theory to applications
[s. l.] : ICLR, 2021. - pp. 1-15