66 citations · 362 across the 77 of their papers we have counts for
17 papers · 1 filter
Minimax Additive Regression under Unknown Dependent Designs
Baptiste Ferrere, Fabrice Gamboa, Jean-Michel Loubes
We study additive regression under an unknown and potentially non product design distribution, allowing the number of covariates to grow with the sample size. We consider a coupled…
Gaussian Processes on Distributions based on Regularized Optimal Transport
François Bachoc, Louis Béthune, Alberto Gonzalez-Sanz +1
We present a novel kernel over the space of probability measures based on the dual formulation of optimal regularized transport. We propose an Hilbertian embedding of the space of…
GAN Estimation of Lipschitz Optimal Transport Maps
Alberto González-Sanz, Lucas de Lara, Louis Béthune +1
This paper introduces the first statistically consistent estimator of the optimal transport map between two probability distributions, based on neural networks. Building on theoret…
Diversity-Preserving K-Armed Bandits, Revisited
Hédi Hadiji, Sébastien Gerchinovitz, Jean-Michel Loubes +1
We consider the bandit-based framework for diversity-preserving recommendations introduced by Celis et al. (2019), who approached it in the case of a polytope mainly by a reduction…
Review of Mathematical frameworks for Fairness in Machine Learning
Eustasio del Barrio, Paula Gordaliza, Jean-Michel Loubes
A review of the main fairness definitions and fair learning methodologies proposed in the literature over the last years is presented from a mathematical point of view. Following o…
Robust spectral clustering using LASSO regularization
Camille Champion, Blazère Mélanie, Burcelin Rémy +2
Cluster structure detection is a fundamental task for the analysis of graphs, in order to understand and to visualize their functional characteristics. Among the different cluster…
A survey of bias in Machine Learning through the prism of Statistical Parity for the Adult Data Set
Philippe Besse, Eustasio del Barrio, Paula Gordaliza +2
Applications based on Machine Learning models have now become an indispensable part of the everyday life and the professional world. A critical question then recently arised among…
Tackling Algorithmic Bias in Neural-Network Classifiers using Wasserstein-2 Regularization
Laurent Risser, Alberto Gonzalez Sanz, Quentin Vincenot +1
The increasingly common use of neural network classifiers in industrial and social applications of image analysis has allowed impressive progress these last years. Such methods are…
optimalFlow: Optimal-transport approach to flow cytometry gating and population matching
Eustasio del Barrio, Hristo Inouzhe, Jean-Michel Loubes +2
Data obtained from Flow Cytometry present pronounced variability due to biological and technical reasons. Biological variability is a well-known phenomenon produced by measurements…
Attraction-Repulsion clustering with applications to fairness
Eustasio del Barrio, Hristo Inouzhe, Jean-Michel Loubes
We consider the problem of diversity enhancing clustering, i.e, developing clustering methods which produce clusters that favour diversity with respect to a set of protected attrib…
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,…
Confidence Intervals for Testing Disparate Impact in Fair Learning
Philippe Besse, Eustasio del Barrio, Paula Gordaliza +1
We provide the asymptotic distribution of the major indexes used in the statistical literature to quantify disparate treatment in machine learning. We aim at promoting the use of c…
Gaussian Processes indexed on the symmetric group: prediction and learning
François Bachoc, Baptiste Broto, Fabrice Gamboa +1
In the framework of the supervised learning of a real function defined on a space X , the so called Kriging method stands on a real Gaussian field defined on X. The Euclidean case…
Multiple testing for outlier detection in functional data
Clémentine Barreyre, Béatrice Laurent, Jean-Michel Loubes +2
We propose a novel procedure for outlier detection in functional data, in a semi-supervised framework. As the data is functional, we consider the coefficients obtained after projec…
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…
Destination Prediction by Trajectory Distribution Based Model
Philippe C. Besse, Brendan Guillouet, Jean-Michel Loubes +1
In this paper we propose a new method to predict the final destination of vehicle trips based on their initial partial trajectories. We first review how we obtained clustering of t…
Review and Perspective for Distance Based Trajectory Clustering
Philippe Besse, Brendan Guillouet, Jean-Michel Loubes +1
In this paper we tackle the issue of clustering trajectories of geolocalized observations. Using clustering technics based on the choice of a distance between the observations, we…