| Lattice signatures and bimodal Gaussians L Ducas, A Durmus, T Lepoint, V Lyubashevsky Advances in Cryptology–CRYPTO 2013: 33rd Annual Cryptology Conference, Santa …, 2013 | 622 | 2013 |
| Nonasymptotic convergence analysis for the unadjusted Langevin algorithm A Durmus, E Moulines | 332 | 2017 |
| High-dimensional Bayesian inference via the unadjusted Langevin algorithm A Durmus, E Moulines | 258 | 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 | 164 | 2018 |
| Analysis of Langevin Monte Carlo via convex optimization A Durmus, S Majewski, B Miasojedow The Journal of Machine Learning Research 20 (1), 2666-2711, 2019 | 143 | 2019 |
| Bridging the gap between constant step size stochastic gradient descent and markov chains A Dieuleveut, A Durmus, F Bach The Annals of Statistics, 2020 | 137 | 2020 |
| Ring-LWE in polynomial rings L Ducas, A Durmus Public Key Cryptography–PKC 2012: 15th International Conference on Practice …, 2012 | 88 | 2012 |
| 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 | 87 | 2019 |
| Irreducibility and geometric ergodicity of Hamiltonian Monte Carlo A Durmus, É Moulines, E Saksman | 84* | 2020 |
| 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 | 69 | 2018 |
| 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 | 59 | 2017 |
| 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 | 56 | 2020 |
| The tamed unadjusted Langevin algorithm N Brosse, A Durmus, É Moulines, S Sabanis Stochastic Processes and their Applications 129 (10), 3638-3663, 2019 | 55 | 2019 |
| Sampling from a strongly log-concave distribution with the Unadjusted Langevin Algorithm A Durmus, E Moulines | 55 | 2016 |
| Piecewise deterministic Markov processes and their invariant measures A Durmus, A Guillin, P Monmarché Annales de l'Institut Henri Poincare (B) Probabilites et statistiques 57 (3 …, 2021 | 49 | 2021 |
| 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 | 47 | 2019 |
| Hypocoercivity of piecewise deterministic Markov process-Monte Carlo C Andrieu, A Durmus, N Nüsken, J Roussel The Annals of Applied Probability 31 (5), 2478-2517, 2021 | 44 | 2021 |
| Geometric ergodicity of the bouncy particle sampler A Durmus, A Guillin, P Monmarché | 44 | 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 | 37 | 2020 |
| Stochastic gradient richardson-romberg markov chain monte carlo A Durmus, U Simsekli, E Moulines, R Badeau, G Richard Advances in Neural Information Processing Systems 29, 2016 | 35 | 2016 |
Eric MoulinesProfesseur, Ecole Polytechnique, Membre de l'Académie des SciencesAdresse e-mail validée de polytechnique.edu
