Extraction of Nystagmus Patterns from Eye-Tracker Data with Convolutional Sparse Coding. - Clément Lalanne; Maxence Rateaux; Laurent Oudre; Matthieu P. Robert; Thomas Moreau 001 (EMBC, 2020)

Private Quantiles Estimation in the Presence of Atoms. - Clément Lalanne; Clément Gastaud; Nicolas Grislain; Aurélien Garivier; Rémi Gribonval (CoRR, 2022)

Private Statistical Estimation of Many Quantiles. - Clément Lalanne; Aurélien Garivier; Rémi Gribonval (ICML, 2023)

Sparsity in neural networks can increase their privacy. - Antoine Gonon; Léon Zheng; Clément Lalanne; Quoc-Tung Le; Guillaume Lauga; Can Pouliquen (CoRR, 2023)

About the Cost of Global Privacy in Density Estimation. - Clément Lalanne; Aurélien Garivier; Rémi Gribonval (CoRR, 2023)

On the Statistical Complexity of Estimation and Testing under Privacy Constraints. - Clément Lalanne; Aurélien Garivier; Rémi Gribonval (Trans. Mach. Learn. Res., 2023)

About the Cost of Central Privacy in Density Estimation. - Clément Lalanne; Aurélien Garivier; Rémi Gribonval (Trans. Mach. Learn. Res., 2023)

On the tradeoffs of statistical learning with privacy. (Sur les compromis liés à l'apprentissage statistique sous contraintes de confidentialité). - Clément Lalanne (2023)

Privately Learning Smooth Distributions on the Hypercube by Projections. - Clément Lalanne; Sébastien Gadat (ICML, 2024)

Learning with Differentially Private (Sliced) Wasserstein Gradients. - David Rodríguez-Vítores; Clément Lalanne; Jean-Michel Loubes (CoRR, 2025)