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math.ST2025
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…
cs.LG2025
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…