Conditionally Gaussian PAC-Bayes

Eugenio Clerico; George Deligiannidis; Arnaud Doucet

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arXiv · 2110.11886

Conditionally Gaussian PAC-Bayes

Eugenio Clerico·George Deligiannidis·Arnaud Doucet

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Abstract

Recent studies have empirically investigated different methods to train stochastic neural networks on a classification task by optimising a PAC-Bayesian bound via stochastic gradient descent. Most of these procedures need to replace the misclassification error with a surrogate loss, leading to a mismatch between the optimisation objective and the actual generalisation bound. The present paper proposes a novel training algorithm that optimises the PAC-Bayesian bound, without relying on any surrogate loss. Empirical results show that this approach outperforms currently available PAC-Bayesian training methods.

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Eugenio Clerico, George Deligiannidis, Arnaud Doucet. 2021-10-22. Conditionally Gaussian PAC-Bayes. https://arxiv.org/abs/2110.11886

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