4 papers
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…
Token-Efficient Change Detection in LLM APIs
Timothée Chauvin, Clément Lalanne, Erwan Le Merrer +3
Remote change detection in LLMs is a difficult problem. Existing methods are either too expensive for deployment at scale, or require initial white-box access to model weights or g…
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…
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…