Maxence Noble

According to our database, Maxence Noble authored at least 12 papers between 2022 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Sampling from multi-modal distributions on Riemannian manifolds with training-free stochastic interpolants.

Alain Durmus, Maxence Noble, Thibaut Pellerin

CoRR, February, 2026

Diffusion-based Annealed Boltzmann Generators : benefits, pitfalls and hopes.

Louis Grenioux, Maxence Noble

CoRR, January, 2026

Fast, faithful and photorealistic diffusion-based image super-resolution with enhanced Flow Map models.

Maxence Noble, Gonzalo Iñaki Quintana, Benjamin Aubin, Clément Chadebec

CoRR, January, 2026

2025

Improving the evaluation of samplers on multi-modal targets.

Louis Grenioux, Maxence Noble, Marylou Gabrié

CoRR, April, 2025

Learned Reference-based Diffusion Sampler for multi-modal distributions.

Maxence Noble, Louis Grenioux, Marylou Gabrié, Alain Oliviero Durmus

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Learned Reference-based Diffusion Sampling for multi-modal distributions.

Maxence Noble, Louis Grenioux, Marylou Gabrié, Alain Oliviero Durmus

CoRR, 2024

Stochastic Localization via Iterative Posterior Sampling.

Louis Grenioux, Maxence Noble, Marylou Gabrié, Alain Oliviero Durmus

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Tree-Based Diffusion Schrödinger Bridge with Applications to Wasserstein Barycenters.

Maxence Noble, Valentin De Bortoli, Arnaud Doucet, Alain Durmus

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Unbiased constrained sampling with Self-Concordant Barrier Hamiltonian Monte Carlo.

Maxence Noble, Valentin De Bortoli, Alain Durmus

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Non-asymptotic convergence bounds for Sinkhorn iterates and their gradients: a coupling approach.

Giacomo Greco, Maxence Noble, Giovanni Conforti, Alain Durmus

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022

Barrier Hamiltonian Monte Carlo.

Maxence Noble, Valentin De Bortoli, Alain Durmus

CoRR, 2022

Differentially Private Federated Learning on Heterogeneous Data.

Maxence Noble, Aurélien Bellet, Aymeric Dieuleveut

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022