2 citations · 3 across the 7 of their papers we have counts for
3 papers · 1 filter
On the disintegration of the stochastic majority vote: From PAC-Bayesian bounds to a self-bounding algorithm
Julien Bastian, Benjamin Leblanc, Pascal Germain +4
Weighted majority votes are central to many successful ensemble methods. PAC-Bayesian theory provides tight generalization guarantees for such models by analyzing the expected risk…
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…