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PAC-Bayes and Information Complexity
Pradeep Kumar Banerjee and Guido MontúfarAbstract
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.
- Received:
- 20.09.21
- Published:
- 20.09.21
- MSC Codes:
- 68Q32, 68T05, 94A15
- Keywords:
- PAC-Bayes generalization bounds, Gibbs algorithm, flat minima
Related publications
PAC-bayes and information complexity
In: ICLR 2021 workshop on neural compression : from information theory to applications[s. l.] : ICLR, 2021. - pp. 1-15