66 citations · 362 across the 77 of their papers we have counts for
87 papers
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…
An Explainable GNN Framework for Component-Level Anomaly Diagnosis
Sena Ozgunay, Louise Travé-Massuyès, Louise Trav{é}-Massuy{è}s +2
Industrial processes are complex systems composed of multiple interacting sensors that generate multivariate time series (MTS). Detecting anomalies in such systems is critical for…
Discovering Geometric Biases in 3D Face Reconstruction: A Curvature-Aware Spectral Framework for Fairness Evaluation
Veronika Shilova, Emmanuel Malherbe, Giovanni Palma +3
3D Morphable Models (3DMMs) remain the standard parametric shape priors for many state-of-the-art 3D face reconstruction algorithms. However, as these models are derived from a fin…
Wasserstein Spatial Depth
François Bachoc, Alberto González-Sanz, Jean-Michel Loubes +1
Modeling observations as random distributions embedded within Wasserstein spaces is becoming increasingly popular across scientific fields, as it captures the variability and geome…
Improved learning theory for kernel distribution regression with two-stage sampling
François Bachoc, Louis Béthune, Alberto González-Sanz +1
The distribution regression problem encompasses many important statistics and machine learning tasks, and arises in a large range of applications. Among various existing approaches…
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…