On sampling from a log-concave density using kinetic Langevin diffusions AS Dalalyan, L Riou-Durand
163 2020 Bounding the error of discretized Langevin algorithms for non-strongly log-concave targets AS Dalalyan, A Karagulyan, L Riou-Durand
Journal of Machine Learning Research 23 (235), 1-38, 2022
45 2022 Noise contrastive estimation: Asymptotic properties, formal comparison with MC-MLE L Riou-Durand, N Chopin
16 * 2018 Nested : Assessing the convergence of Markov chain Monte Carlo when running many short chains CC Margossian, MD Hoffman, P Sountsov, L Riou-Durand, A Vehtari, ...
arXiv preprint arXiv:2110.13017, 2021
12 2021 Metropolis adjusted Langevin trajectories: a robust alternative to Hamiltonian Monte Carlo L Riou-Durand, J Vogrinc
arXiv preprint arXiv:2202.13230, 2022
8 2022 Adaptive tuning for Metropolis adjusted Langevin trajectories L Riou-Durand, P Sountsov, J Vogrinc, C Margossian, S Power
International Conference on Artificial Intelligence and Statistics, 8102-8116, 2023
4 2023 Theoretical contributions to Monte Carlo methods, and applications to Statistics L Riou-Durand
Université Paris-Saclay, 2019
2019 Assessing the Convergence of Markov chain Monte Carlo when running many short chains CC Margossian, MD Hoffman, P Sountsov, L Riou-Durand, A Vehtari, ...
Bayesian inference using sub-posteriors. L Riou-Durand