Differentially Private Federated Learning on Heterogeneous Data. - Maxence Noble; Aurélien Bellet; Aymeric Dieuleveut (AISTATS, 2022)

Barrier Hamiltonian Monte Carlo. - Maxence Noble; Valentin De Bortoli; Alain Durmus (CoRR, 2022)

Non-asymptotic convergence bounds for Sinkhorn iterates and their gradients: a coupling approach. - Giacomo Greco; Maxence Noble; Giovanni Conforti; Alain Durmus (COLT, 2023)

Unbiased constrained sampling with Self-Concordant Barrier Hamiltonian Monte Carlo. - Maxence Noble; Valentin De Bortoli; Alain Durmus (NeurIPS, 2023)

Tree-Based Diffusion Schrödinger Bridge with Applications to Wasserstein Barycenters. - Maxence Noble; Valentin De Bortoli; Arnaud Doucet; Alain Durmus (NeurIPS, 2023)

Stochastic Localization via Iterative Posterior Sampling. - Louis Grenioux; Maxence Noble; Marylou Gabrié; Alain Oliviero Durmus (ICML, 2024)

Learned Reference-based Diffusion Sampling for multi-modal distributions. - Maxence Noble; Louis Grenioux; Marylou Gabrié; Alain Oliviero Durmus (CoRR, 2024)

Learned Reference-based Diffusion Sampler for multi-modal distributions. - Maxence Noble; Louis Grenioux; Marylou Gabrié; Alain Oliviero Durmus (ICLR, 2025)

Improving the evaluation of samplers on multi-modal targets. - Louis Grenioux; Maxence Noble; Marylou Gabrié (CoRR, 2025)