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…