2 citations · 2 across the 3 of their papers we have counts for
4 papers
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…
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…
PAC-Bayesian Bounds on Constrained f-Entropic Risk Measures
Hind Atbir, Farah Cherfaoui, Guillaume Metzler +2
PAC generalization bounds on the risk, when expressed in terms of the expected loss, are often insufficient to capture imbalances between subgroups in the data. To overcome this li…
An Adjusted Nearest Neighbor Algorithm Maximizing the F-Measure from Imbalanced Data
Rémi Viola, Rémi Emonet, Amaury Habrard +3
In this paper, we address the challenging problem of learning from imbalanced data using a Nearest-Neighbor (NN) algorithm. In this setting, the minority examples typically belong…