14 citations · 14 across the 3 of their papers we have counts for
6 papers
On the Comparison of Optimizers for Imbalanced Learning
Cl{é}ment Lezane, Fran{\c c}ois Bachoc, J{é}r{ô}me Bolte +1
Data imbalance is pervasive in machine learning, from rare words and anomalies to underrepresented patterns in heterogeneous or cross-tabulated data. We study idealized optimizers…
When majority rules, minority loses: bias amplification of gradient descent
François Bachoc, Jérôme Bolte, Ryan Boustany +1
Despite growing empirical evidence of bias amplification in machine learning, its theoretical foundations remain poorly understood. We develop a formal framework for majority-minor…
Bayesian Regression and Classification Using Gaussian Process Priors Indexed by Probability Density Functions
A. Fradi, Y. Feunteun, C. Samir +3
In this paper, we introduce the notion of Gaussian processes indexed by probability density functions for extending the Matérn family of covariance functions. We use some tools fro…
Explaining Machine Learning Models using Entropic Variable Projection
François Bachoc, Fabrice Gamboa, Max Halford +2
In this paper, we present a new explainability formalism designed to shed light on how each input variable of a test set impacts the predictions of machine learning models. Hence,…
Gaussian processes with multidimensional distribution inputs via optimal transport and Hilbertian embedding
Francois Bachoc, Alexandra Suvorikova, David Ginsbourger +2
In this work, we investigate Gaussian Processes indexed by multidimensional distributions. While directly constructing radial positive definite kernels based on the Wasserstein dis…
A Gaussian Process Regression Model for Distribution Inputs
François Bachoc, Fabrice Gamboa, Jean-Michel Loubes +1
Monge-Kantorovich distances, otherwise known as Wasserstein distances, have received a growing attention in statistics and machine learning as a powerful discrepancy measure for pr…