12 citations · 25 across the 30 of their papers we have counts for
32 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…
OT-FairBoost: Optimal Transport-Guided Gradient Boosting for Fairness Regularization on Tabular Data
Veronika Shilova, Abdoulaye Sakho, Younes Boumoussou +3
Although neural-based machine learning models have received a lot of attention recently, tree-based models such as gradient boosting are competitive for tabular data and therefore…
Minimax Private Estimation of Smooth Optimal-Transport Maps
Clément Lalanne, David Rodríguez-Vítores, Franck Iutzeler +1
We study the problem of estimating smooth optimal transport (OT) maps between two probability distributions under differential privacy (DP) constraints. Leveraging wavelet-based de…
Generalized Functional ANOVA: A Complete Theoretical Framework
Baptiste Ferrere, Nicolas Bousquet, Fabrice Gamboa +1
The functional ANOVA provides a fundamental representation of square-integrable multivariate functions into main effects and higher-order interactions. For independent inputs, the…
Evaluating Black-Box Vulnerabilities with Wasserstein-Constrained Data Perturbations
Adriana Laurindo Monteiro, Jean-Michel Loubes
The growing use of Machine Learning (ML) tools comes with critical challenges, such as limited model explainability. We propose a global explainability framework that leverages Opt…
Homophily-aware Supervised Contrastive Counterfactual Augmented Fair Graph Neural Network
Mahdi Tavassoli Kejani, Fadi Dornaika, Charlotte Laclau +1
In recent years, Graph Neural Networks (GNNs) have achieved remarkable success in tasks such as node classification, link prediction, and graph representation learning. However, th…
Probing Cultural Signals in Large Language Models through Author Profiling
Valentin Lafargue, Ariel Guerra-Adames, Emmanuelle Claeys +2
Large language models (LLMs) are increasingly deployed in applications with societal impact, raising concerns about the cultural biases they encode. We probe these representations…
Exact Functional ANOVA Decomposition for Categorical Inputs Models
Baptiste Ferrere, Nicolas Bousquet, Fabrice Gamboa +2
Functional ANOVA offers a principled framework for interpretability by decomposing a model's prediction into main effects and higher-order interactions. For independent features, t…
Token-Efficient Change Detection in LLM APIs
Timothée Chauvin, Clément Lalanne, Erwan Le Merrer +3
Remote change detection in LLMs is a difficult problem. Existing methods are either too expensive for deployment at scale, or require initial white-box access to model weights or g…
Model-Agnostic Fairness Regularization for GNNs with Incomplete Sensitive Information
Mahdi Tavassoli Kejani, Fadi Dornaika, Jean-Michel Loubes
Graph Neural Networks (GNNs) have demonstrated exceptional efficacy in relational learning tasks, including node classification and link prediction. However, their application rais…
Buzz, Choose, Forget: A Meta-Bandit Framework for Bee-Like Decision Making
Emmanuelle Claeys, Elena Kerjean, Jean-Michel Loubes
This work introduces MAYA, a sequential imitation learning model based on multi-armed bandits, designed to reproduce and predict individual bees' decisions in contextualized foragi…
Fourier Analysis on the Boolean Hypercube via Hoeffding Functional Decomposition
Baptiste Ferrere, Nicolas Bousquet, Fabrice Gamboa +2
Fourier analysis on the Boolean hypercube is fundamentally defined as the orthogonal decomposition of the space of pseudo-Boolean functions with respect to the uniform probability…
Fairness-Aware Grouping for Continuous Sensitive Variables: Application for Debiasing Face Analysis with respect to Skin Tone
Veronika Shilova, Emmanuel Malherbe, Giovanni Palma +2
Within a legal framework, fairness in datasets and models is typically assessed by dividing observations into predefined groups and then computing fairness measures (e.g., Disparat…
Exposing the Illusion of Fairness: Auditing Vulnerabilities to Distributional Manipulation Attacks
Valentin Lafargue, Adriana Laurindo Monteiro, Emmanuelle Claeys +2
The rapid deployment of AI systems in high-stakes domains, including those classified as high-risk under the The EU AI Act (Regulation (EU) 2024/1689), has intensified the need for…
Distributional Limit Theory for Optimal Transport
Eustasio del Barrio, Alberto González-Sanz, Jean-Michel Loubes +1
Optimal Transport (OT) is a resource allocation problem with applications in biology, data science, economics and statistics, among others. In some of the applications, practitione…
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…
Fairness is in the details: Face Dataset Auditing
Valentin Lafargue, Emmanuelle Claeys, Jean-Michel Loubes
Auditing involves verifying the proper implementation of a given policy. As such, auditing is essential for ensuring compliance with the principles of fairness, equity, and transpa…
An improved central limit theorem for the empirical sliced Wasserstein distance
David Rodríguez-Vítores, Eustasio del Barrio, Jean-Michel Loubes
Wasserstein distances are widely used in modern data analysis but pose significant computational and statistical challenges in high dimensions. The sliced Wasserstein distance alle…
PLS-based approach for fair representation learning
Elena M. De-Diego, Adrián Perez-Suay, Paula Gordaliza +1
We revisit the problem of fair representation learning by proposing Fair Partial Least Squares (PLS) components. PLS is widely used in statistics to efficiently reduce the dimensio…
On the Private Estimation of Smooth Transport Maps
Clément Lalanne, Franck Iutzeler, Jean-Michel Loubes +1
Estimating optimal transport maps between two distributions from respective samples is an important element for many machine learning methods. To do so, rather than extending discr…
Learning with Differentially Private (Sliced) Wasserstein Gradients
David Rodríguez-Vítores, Clément Lalanne, Jean-Michel Loubes
In this work, we introduce a novel framework for privately optimizing objectives that rely on Wasserstein distances between data-dependent empirical measures. Our main theoretical…
Fair Graph Neural Network with Supervised Contrastive Regularization
Mahdi Tavassoli Kejani, Fadi Dornaika, Jean-Michel Loubes
In recent years, Graph Neural Networks (GNNs) have made significant advancements, particularly in tasks such as node classification, link prediction, and graph representation. Howe…
Debiasing Machine Learning Models by Using Weakly Supervised Learning
Renan D. B. Brotto, Jean-Michel Loubes, Laurent Risser +3
We tackle the problem of bias mitigation of algorithmic decisions in a setting where both the output of the algorithm and the sensitive variable are continuous. Most of prior work…
Hoeffding decomposition of black-box models with dependent inputs
Marouane Il Idrissi, Nicolas Bousquet, Fabrice Gamboa +2
Performing an additive decomposition of arbitrary functions of random elements is paramount for global sensitivity analysis and, therefore, the interpretation of black-box models.…