12 citations · 17 across the 25 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
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…
Central Limit Theorems for General Transportation Costs
Eustasio del Barrio, Alberto González-Sanz, Jean-Michel Loubes
We consider the problem of optimal transportation with general cost between a empirical measure and a general target probability on R d , with d 1. We extend results in [19]…