26 citations · 40 across the 14 of their papers we have counts for
16 papers
Are fairness metric scores enough to assess discrimination biases in machine learning?
Fanny Jourdan, Laurent Risser, Jean-Michel Loubes +1
This paper presents novel experiments shedding light on the shortcomings of current metrics for assessing biases of gender discrimination made by machine learning algorithms on tex…
COCKATIEL: COntinuous Concept ranKed ATtribution with Interpretable ELements for explaining neural net classifiers on NLP tasks
Fanny Jourdan, Agustin Picard, Thomas Fel +3
Transformer architectures are complex and their use in NLP, while it has engendered many successes, makes their interpretability or explainability challenging. Recent debates have…
How optimal transport can tackle gender biases in multi-class neural-network classifiers for job recommendations?
Fanny Jourdan, Titon Tshiongo Kaninku, Nicholas Asher +2
Automatic recommendation systems based on deep neural networks have become extremely popular during the last decade. Some of these systems can however be used for applications whic…
A survey of Identification and mitigation of Machine Learning algorithmic biases in Image Analysis
Laurent Risser, Agustin Picard, Lucas Hervier +1
The problem of algorithmic bias in machine learning has gained a lot of attention in recent years due to its concrete and potentially hazardous implications in society. In much the…
Physics-informed Variational Autoencoders for Improved Robustness to Environmental Factors of Variation
Romain Thoreau, Laurent Risser, Véronique Achard +2
The combination of machine learning models with physical models is a recent research path to learn robust data representations. In this paper, we introduce pVAE, a variational…
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…
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,…
COREclust: a new package for a robust and scalable analysis of complex data
Camille Champion, Anne-Claire Brunet, Jean-Michel Loubes +1
In this paper, we present a new R package COREclust dedicated to the detection of representative variables in high dimensional spaces with a potentially limited number of observati…
Online Barycenter Estimation of Large Weighted Graphs
Ioana Gavra, Laurent Risser
In this paper, we propose a new method to compute the barycenter of large weighted graphs endowed with probability measures on their nodes. We suppose that the edge weights are dis…
Cytometry inference through adaptive atomic deconvolution
Manon Costa, Sébastien Gadat, Pauline Gonnord +1
In this paper we consider a statistical estimation problem known as atomic deconvolution. Introduced in reliability, this model has a direct application when considering biological…
How to compute the barycenter of a weighted graph
Sébastien Gadat, Ioana Gavra, Laurent Risser
Discrete structures like graphs make it possible to naturally and flexibly model complex phenomena. Since graphs that represent various types of information are increasingly availa…
Anisotropic Diffusion in ITK
Jean-Marie Mirebeau, Jérôme Fehrenbach, Laurent Risser +1
Anisotropic Non-Linear Diffusion is a powerful image processing technique, which allows to simultaneously remove the noise and enhance sharp features in two or three dimensional im…
Diffeomorphic image matching with left-invariant metrics
Tanya Schmah, Laurent Risser, François-Xavier Vialard
The geometric approach to diffeomorphic image registration known as "large deformation by diffeomorphic metric mapping" (LDDMM) is based on a left action of diffeomorphisms on imag…
Mixture of Kernels and Iterated Semidirect Product of Diffeomorphisms Groups
Martins Bruveris, Laurent Risser, François-Xavier Vialard
In the framework of large deformation diffeomorphic metric mapping (LDDMM), we develop a multi-scale theory for the diffeomorphism group based on previous works. The purpose of the…