From the 1 of 4 linked papers with an AI index.
4 papers
Distributional Limit Theory for Optimal Transport
Eustasio del Barrio, Alberto González-Sanz, Jean-Michel Loubes +1
The paper surveys recent theoretical results on the statistical behavior of empirical optimal transport quantities, such as plans, maps, and costs, and discusses how to construct c…
Minimax Private Estimation of Smooth Optimal-Transport Maps
Clément Lalanne, David RodrÃguez-VÃtores, 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…
An improved central limit theorem for the empirical sliced Wasserstein distance
David RodrÃguez-VÃtores, 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…
Learning with Differentially Private (Sliced) Wasserstein Gradients
David RodrÃguez-VÃtores, 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…