14 citations · 14 across the 3 of their papers we have counts for
3 papers · 1 filter
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…
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,…
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…