Statistical M-Estimation and Consistency in Large Deformable Models for Image Warping. - Jérémie Bigot; Sébastien Gadat; Jean-Michel Loubes (J. Math. Imaging Vis., 2009)

Group Lasso Estimation of High-dimensional Covariance Matrices. - Jérémie Bigot; Rolando J. Biscay; Jean-Michel Loubes; Lilian Muñiz-Alvarez (J. Mach. Learn. Res., 2011)

Non parametric estimation of the structural expectation of a stochastic increasing function. - Jean-François Dupuy; Jean-Michel Loubes; Elie Maza (Stat. Comput., 2011)

Regularization with Approximated L 2 Maximum Entropy Method. - Jean-Michel Loubes; Paul Rochet (Mathematical Methods for Signal and Image Analysis and Representation, 2012)

Big Data - Retour vers le Futur 3; De Statisticien à Data Scientist. - Philippe C. Besse; Aurélien Garivier; Jean-Michel Loubes (CoRR, 2014)

A robust algorithm for template curve estimation based on manifold embedding. - Chloé Dimeglio; Santiago Gallón; Jean-Michel Loubes; Elie Maza (Comput. Stat. Data Anal., 2014)

Big data analytics - Retour vers le futur 3. De statisticien à data scientist. - Philippe C. Besse; Aurélien Garivier; Jean-Michel Loubes (Ingénierie des Systèmes d Inf., 2014)

Estimation of covariance functions by a fully data-driven model selection procedure and its application to Kriging spatial interpolation of real rainfall data. - Rolando Biscay Lirio; Dunia Giniebra Camejo; Jean-Michel Loubes; Lilian Muñiz-Alvarez (Stat. Methods Appl., 2014)

Oracle Inequalities for a Group Lasso Procedure Applied to Generalized Linear Models in High Dimension. - Mélanie Blazère; Jean-Michel Loubes; Fabrice Gamboa (IEEE Trans. Inf. Theory, 2014)

Barycenter in Wasserstein Spaces: Existence and Consistency. - Thibaut Le Gouic; Jean-Michel Loubes (GSI, 2015)

Review and Perspective for Distance Based Trajectory Clustering. - Philippe C. Besse; Brendan Guillouet; Jean-Michel Loubes; François Royer (CoRR, 2015)

A parametric registration model for warped distributions with Wasserstein's distance. - Marina Agulló-Antolín; Juan Antonio Cuesta-Albertos; Hélène Lescornel; Jean-Michel Loubes (J. Multivar. Anal., 2015)

Big Data analytics. Three use cases with R, Python and Spark. - Philippe C. Besse; Brendan Guillouet; Jean-Michel Loubes (CoRR, 2016)

Review and Perspective for Distance-Based Clustering of Vehicle Trajectories. - Philippe C. Besse; Brendan Guillouet; Jean-Michel Loubes; Francois Royer (IEEE Trans. Intell. Transp. Syst., 2016)

Deep Learning applied to Road Traffic Speed forecasting. - Thomas Epelbaum; Fabrice Gamboa; Jean-Michel Loubes; Jessica Martin (CoRR, 2017)

Supervised Learning Approach for Surface-Mount Device Production. - Eva Jabbar; Philippe C. Besse; Jean-Michel Loubes; Nathalie Barbosa Roa; Christophe Merle; Rémi Dettai (LOD, 2018)

COREclust: a new package for a robust and scalable analysis of complex data. - Camille Champion; Anne-Claire Brunet; Jean-Michel Loubes; Laurent Risser (CoRR, 2018)

Confidence Intervals for Testing Disparate Impact in Fair Learning. - Philippe C. Besse; Eustasio del Barrio; Paula Gordaliza; Jean-Michel Loubes (CoRR, 2018)

Can everyday AI be ethical. Fairness of Machine Learning Algorithms. - Philippe C. Besse; Céline Castets-Renard; Aurélien Garivier; Jean-Michel Loubes (CoRR, 2018)

Entropic Variable Boosting for Explainability and Interpretability in Machine Learning. - François Bachoc; Fabrice Gamboa; Jean-Michel Loubes; Laurent Risser (CoRR, 2018)

A Gaussian Process Regression Model for Distribution Inputs. - François Bachoc; Fabrice Gamboa; Jean-Michel Loubes; Nil Venet (IEEE Trans. Inf. Theory, 2018)

Destination Prediction by Trajectory Distribution-Based Model. - Philippe C. Besse; Brendan Guillouet; Jean-Michel Loubes; Francois Royer (IEEE Trans. Intell. Transp. Syst., 2018)

Obtaining Fairness using Optimal Transport Theory. - Paula Gordaliza; Eustasio del Barrio; Fabrice Gamboa; Jean-Michel Loubes (ICML, 2019)

Conditional Anomaly Detection for Quality and Productivity Improvement of Electronics Manufacturing Systems. - Eva Jabbar; Philippe C. Besse; Jean-Michel Loubes; Christophe Merle (LOD, 2019)

Learning a Gaussian Process Model on the Riemannian Manifold of Non-decreasing Distribution Functions. - Chafik Samir; Jean-Michel Loubes; Anne-Françoise Yao; François Bachoc (PRICAI (2), 2019)

Attraction-Repulsion clustering with applications to fairness. - Eustasio del Barrio; Hristo Inouzhe; Jean-Michel Loubes (CoRR, 2019)

Using Wasserstein-2 regularization to ensure fair decisions with Neural-Network classifiers. - Laurent Risser; Quentin Vincenot; Nicolas P. Couellan; Jean-Michel Loubes (CoRR, 2019)

Central limit theorem and bootstrap procedure for Wasserstein's variations with an application to structural relationships between distributions. - Eustasio del Barrio; Paula Gordaliza; Hélène Lescornel; Jean-Michel Loubes (J. Multivar. Anal., 2019)

optimalFlow: optimal transport approach to flow cytometry gating and population matching. - Eustasio del Barrio; Hristo Inouzhe; Jean-Michel Loubes; Carlos Matrán; Agustín Mayo-Íscar (BMC Bioinform., 2020)

Minimax optimal goodness-of-fit testing for densities under a local differential privacy constraint. - Joseph Lam-Weil; Béatrice Laurent; Jean-Michel Loubes (CoRR, 2020)

A survey of bias in Machine Learning through the prism of Statistical Parity for the Adult Data Set. - Philippe C. Besse; Eustasio del Barrio; Paula Gordaliza; Jean-Michel Loubes; Laurent Risser (CoRR, 2020)

Robust spectral clustering using LASSO regularization. - Camille Champion; Mélanie Blazère; Rémy Burcelin; Jean-Michel Loubes; Laurent Risser (CoRR, 2020)

The price for fairness in a regression framework. - Thibaut Le Gouic; Jean-Michel Loubes (CoRR, 2020)

Review of Mathematical frameworks for Fairness in Machine Learning. - Eustasio del Barrio; Paula Gordaliza; Jean-Michel Loubes (CoRR, 2020)

The statistical effect of entropic regularization in optimal transportation. - Eustasio del Barrio; Jean-Michel Loubes (CoRR, 2020)

Multiple Testing for Outlier Detection in Space Telemetries. - Clémentine Barreyre; Béatrice Laurent; Jean-Michel Loubes; Loïc Boussouf; Bertrand Cabon (IEEE Trans. Big Data, 2020)

Achieving Robustness in Classification Using Optimal Transport With Hinge Regularization. - Mathieu Serrurier; Franck Mamalet; Alberto González-Sanz; Thibaut Boissin; Jean-Michel Loubes; Eustasio del Barrio (CVPR, 2021)

Detection of Representative Variables in Complex Systems with Interpretable Rules Using Core-Clusters. - Camille Champion; Anne-Claire Brunet; Rémy Burcelin; Jean-Michel Loubes; Laurent Risser (Algorithms, 2021)

Transport-based Counterfactual Models. - Lucas de Lara; Alberto González-Sanz; Nicholas Asher; Jean-Michel Loubes (CoRR, 2021)

Bayesian regression and classification using Gaussian process priors indexed by probability density functions. - Anis Fradi; Yan Feunteun; Chafik Samir; M. Baklouti; François Bachoc; Jean-Michel Loubes (Inf. Sci., 2021)

GAN Estimation of Lipschitz Optimal Transport Maps. - Alberto González-Sanz; Lucas de Lara; Louis Béthune; Jean-Michel Loubes (CoRR, 2022)

Fairness constraint in Structural Econometrics and Application to fair estimation using Instrumental Variables. - Samuele Centorrino; Jean-Pierre Florens; Jean-Michel Loubes (CoRR, 2022)

Dimension Reduction for time series with Variational AutoEncoders. - William Todo; Béatrice Laurent; Jean-Michel Loubes; Merwann Selmani (CoRR, 2022)

A survey of Identification and mitigation of Machine Learning algorithmic biases in Image Analysis. - Laurent Risser; Agustin Martin Picard; Lucas Hervier; Jean-Michel Loubes (CoRR, 2022)

Tackling Algorithmic Bias in Neural-Network Classifiers using Wasserstein-2 Regularization. - Laurent Risser; Alberto González-Sanz; Quentin Vincenot; Jean-Michel Loubes (J. Math. Imaging Vis., 2022)

Nonparametric Bayesian Regression and Classification on Manifolds, With Applications to 3D Cochlear Shapes. - Anis Fradi; Chafik Samir; José Braga; Shantanu H. Joshi; Jean-Michel Loubes (IEEE Trans. Image Process., 2022)

COCKATIEL: COntinuous Concept ranKed ATtribution with Interpretable ELements for explaining neural net classifiers on NLP. - Fanny Jourdan; Agustin Martin Picard; Thomas Fel; Laurent Risser; Jean-Michel Loubes; Nicholas Asher (ACL (Findings), 2023)

Gaussian Processes on Distributions based on Regularized Optimal Transport. - François Bachoc; Louis Béthune; Alberto González-Sanz; Jean-Michel Loubes (AISTATS, 2023)

Breaking Bias: How Optimal Transport Can Help to Tackle Gender Biases in NLP Based Job Recommendation Systems? - Fanny Jourdan; Titon Tshiongo Kaninku; Nicholas Asher; Jean-Michel Loubes; Laurent Risser (EWAF, 2023)

Is a Fairness Metric Score Enough to Assess Discrimination Biases in Machine Learning? - Fanny Jourdan; Ronan Pons; Nicholas Asher; Jean-Michel Loubes; Laurent Risser (EWAF, 2023)

Counterfactual Explanation for Multivariate Times Series Using A Contrastive Variational Autoencoder. - William Todo; Merwann Selmani; Béatrice Laurent; Jean-Michel Loubes (ICASSP, 2023)

Detecting and Processing Unsuspected Sensitive Variables for Robust Machine Learning. - Laurent Risser; Agustin Martin Picard; Lucas Hervier; Jean-Michel Loubes (Algorithms, 2023)

When Mitigating Bias is Unfair: A Comprehensive Study on the Impact of Bias Mitigation Algorithms. - Natasa Krco; Thibault Laugel; Jean-Michel Loubes; Marcin Detyniecki (CoRR, 2023)

COCKATIEL: COntinuous Concept ranKed ATtribution with Interpretable ELements for explaining neural net classifiers on NLP tasks. - Fanny Jourdan; Agustin Martin Picard; Thomas Fel; Laurent Risser; Jean-Michel Loubes; Nicholas Asher (CoRR, 2023)

Are fairness metric scores enough to assess discrimination biases in machine learning? - Fanny Jourdan; Laurent Risser; Jean-Michel Loubes; Nicholas Asher (CoRR, 2023)

Fair Kernel Regression through Cross-Covariance Operators. - Adrian Perez-Suay; Paula Gordaliza; Jean-Michel Loubes; Dino Sejdinovic; Gustau Camps-Valls (Trans. Mach. Learn. Res., 2023)

How Optimal Transport Can Tackle Gender Biases in Multi-Class Neural Network Classifiers for Job Recommendations. - Fanny Jourdan; Titon Tshiongo Kaninku; Nicholas Asher; Jean-Michel Loubes; Laurent Risser (Algorithms, 2023)

Diffeomorphic Registration Using Sinkhorn Divergences. - Lucas de Lara; Alberto González-Sanz; Jean-Michel Loubes (SIAM J. Imaging Sci., 2023)

An Improved Central Limit Theorem and Fast Convergence Rates for Entropic Transportation Costs. - Eustasio del Barrio; Alberto González-Sanz; Jean-Michel Loubes; Jonathan Niles-Weed (SIAM J. Math. Data Sci., 2023)

Attraction-repulsion clustering: a way of promoting diversity linked to demographic parity in fair clustering. - Eustasio del Barrio; Hristo Inouzhe; Jean-Michel Loubes (Adv. Data Anal. Classif., 2023)

Debiasing Machine Learning Models by Using Weakly Supervised Learning. - Renan D. B. Brotto; Jean-Michel Loubes; Laurent Risser; Jean-Pierre Florens; Kenji Nose Filho; João M. T. Romano (CoRR, 2024)

Fair Graph Neural Network with Supervised Contrastive Regularization. - Mahdi Tavassoli Kejani; Fadi Dornaika; Jean-Michel Loubes (CoRR, 2024)

Transport-based Counterfactual Models. - Lucas de Lara; Alberto González-Sanz; Nicholas Asher; Laurent Risser; Jean-Michel Loubes (J. Mach. Learn. Res., 2024)

Diversity-Preserving K-Armed Bandits, Revisited. - Hédi Hadiji; Sébastien Gerchinovitz; Jean-Michel Loubes; Gilles Stoltz (Trans. Mach. Learn. Res., 2024)

Fairness seen as global sensitivity analysis. - Clément Benesse; Fabrice Gamboa; Jean-Michel Loubes; Thibaut Boissin (Mach. Learn., 2024)

When Mitigating Bias is Unfair: Multiplicity and Arbitrariness in Algorithmic Group Fairness. - Natasa Krco; Thibault Laugel; Vincent Grari; Jean-Michel Loubes; Marcin Detyniecki (SaTML, 2025)

Hoeffding decomposition of functions of random dependent variables. - Marouane Il Idrissi; Nicolas Bousquet 002; Fabrice Gamboa; Bertrand Iooss; Jean-Michel Loubes (J. Multivar. Anal., 2025)

Learning with Differentially Private (Sliced) Wasserstein Gradients. - David Rodríguez-Vítores; Clément Lalanne; Jean-Michel Loubes (CoRR, 2025)

PLS-based approach for fair representation learning. - Elena M. De-Diego; Adrián Pérez-Suay; Paula Gordaliza; Jean-Michel Loubes (CoRR, 2025)

When majority rules, minority loses: bias amplification of gradient descent. - François Bachoc; Jérôme Bolte; Ryan Boustany; Jean-Michel Loubes (CoRR, 2025)

Fairness-Aware Grouping for Continuous Sensitive Variables: Application for Debiasing Face Analysis with respect to Skin Tone. - Veronika Shilova; Emmanuel Malherbe; Giovanni Palma; Laurent Risser; Jean-Michel Loubes (CoRR, 2025)

Exposing the Illusion of Fairness: Auditing Vulnerabilities to Distributional Manipulation Attacks. - Valentin Lafargue; Adriana Laurindo Monteiro; Emmanuelle Claeys; Laurent Risser; Jean-Michel Loubes (CoRR, 2025)