| Lattice signatures and bimodal Gaussians L Ducas, A Durmus, T Lepoint, V Lyubashevsky Annual Cryptology Conference, 40-56, 2013 | 776 | 2013 |
| Nonasymptotic convergence analysis for the unadjusted Langevin algorithm A Durmus, E Moulines | 533 | 2017 |
| High-dimensional Bayesian inference via the unadjusted Langevin algorithm A Durmus, E Moulines | 403 | 2019 |
| Analysis of Langevin Monte Carlo via convex optimization A Durmus, S Majewski, B Miasojedow Journal of Machine Learning Research 20 (73), 1-46, 2019 | 262 | 2019 |
| Efficient bayesian computation by proximal markov chain monte carlo: when langevin meets moreau A Durmus, E Moulines, M Pereyra SIAM Journal on Imaging Sciences 11 (1), 473-506, 2018 | 248 | 2018 |
| Bridging the gap between constant step size stochastic gradient descent and markov chains A Dieuleveut, A Durmus, F Bach The Annals of Statistics, 2020 | 209 | 2020 |
| Sliced-Wasserstein flows: Nonparametric generative modeling via optimal transport and diffusions A Liutkus, U Simsekli, S Majewski, A Durmus, FR Stöter International Conference on machine learning, 4104-4113, 2019 | 152 | 2019 |
| Bayesian imaging using plug & play priors: when langevin meets tweedie R Laumont, VD Bortoli, A Almansa, J Delon, A Durmus, M Pereyra SIAM Journal on Imaging Sciences 15 (2), 701-737, 2022 | 132 | 2022 |
| The promises and pitfalls of stochastic gradient Langevin dynamics N Brosse, A Durmus, E Moulines NeurIPS 2018 (Advances in Neural Information Processing Systems 2018). 2018, 2018 | 114 | 2018 |
| Ring-LWE in polynomial rings L Ducas, A Durmus International Workshop on Public Key Cryptography, 34-51, 2012 | 114 | 2012 |
| Irreducibility and geometric ergodicity of Hamiltonian Monte Carlo A Durmus, É Moulines, E Saksman The Annals of Statistics 48 (6), 3545-3564, 2020 | 108* | 2020 |
| An elementary approach to uniform in time propagation of chaos A Durmus, A Eberle, A Guillin, R Zimmer Proceedings of the American Mathematical Society 148 (12), 5387-5398, 2020 | 105 | 2020 |
| Statistical and topological properties of sliced probability divergences K Nadjahi, A Durmus, L Chizat, S Kolouri, S Shahrampour, U Simsekli Advances in Neural Information Processing Systems 33, 20802-20812, 2020 | 99 | 2020 |
| Sampling from a log-concave distribution with compact support with proximal Langevin Monte Carlo N Brosse, A Durmus, É Moulines, M Pereyra Conference on learning theory, 319-342, 2017 | 94 | 2017 |
| The tamed unadjusted Langevin algorithm N Brosse, A Durmus, É Moulines, S Sabanis Stochastic Processes and their Applications 129 (10), 3638-3663, 2019 | 87 | 2019 |
| Asymptotic guarantees for learning generative models with the sliced-Wasserstein distance K Nadjahi, A Durmus, U Simsekli, R Badeau Advances in Neural Information Processing Systems 32, 2019 | 80 | 2019 |
| Sampling from strongly log-concave distributions with the Unadjusted Langevin Algorithm A Durmus, E Moulines arXiv preprint arXiv:1605.01559 5 (3), 2016 | 68 | 2016 |
| Geometric ergodicity of the bouncy particle sampler A Durmus, A Guillin, P Monmarché The Annals of Applied Probability 30 (5), 2069-2098, 2020 | 67 | 2020 |
| Piecewise deterministic Markov processes and their invariant measures A Durmus, A Guillin, P Monmarché Annales de l'Institut Henri Poincaré (B) Probabilités et Statistiques 57 (3 …, 2021 | 66 | 2021 |
| Maximum likelihood estimation of regularization parameters in high-dimensional inverse problems: An empirical bayesian approach part i: Methodology and experiments AF Vidal, V De Bortoli, M Pereyra, A Durmus SIAM Journal on Imaging Sciences 13 (4), 1945-1989, 2020 | 59 | 2020 |
Eric MoulinesProfesseur, Ecole Polytechnique, Membre de l'Académie des SciencesVerified email at polytechnique.edu
