4 papers
Simplicity Suffices for Parameter Noise Injection in Stochastic Gradient Descent
Benjamin Leblanc, Louis-Jacob Lebel, Teddy Kana +1
Injecting noise into the optimization process is a well-established technique for improving the training and generalization of deep neural networks. Yet, despite the breadth of exi…
PAC-Bayesian Generalization Guarantees for Fairness on Stochastic and Deterministic Classifiers
Julien Bastian, Benjamin Leblanc, Pascal Germain +5
Classical PAC generalization bounds on the prediction risk of a classifier are insufficient to provide theoretical guarantees on fairness when the goal is to learn models balancing…
A Framework for Bounding Deterministic Risk with PAC-Bayes: Applications to Majority Votes
Benjamin Leblanc, Pascal Germain
PAC-Bayes is a popular and efficient framework for obtaining generalization guarantees in situations involving uncountable hypothesis spaces. Unfortunately, in its classical formul…
Generalization Bounds via Meta-Learned Model Representations: PAC-Bayes and Sample Compression Hypernetworks
Benjamin Leblanc, Mathieu Bazinet, Nathaniel D'Amours +2
Both PAC-Bayesian and Sample Compress learning frameworks are instrumental for deriving tight (non-vacuous) generalization bounds for neural networks. We leverage these results in…