Lattice signatures and bimodal Gaussians L Ducas, A Durmus, T Lepoint, V Lyubashevsky Annual Cryptology Conference, 40-56, 2013 | 534 | 2013 |

Nonasymptotic convergence analysis for the unadjusted Langevin algorithm A Durmus, E Moulines The Annals of Applied Probability 27 (3), 1551-1587, 2017 | 229 | 2017 |

High-dimensional Bayesian inference via the unadjusted Langevin algorithm A Durmus, E Moulines Bernoulli 25 (4A), 2854-2882, 2019 | 157 | 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 | 109 | 2018 |

Bridging the gap between constant step size stochastic gradient descent and markov chains A Dieuleveut, A Durmus, F Bach arXiv preprint arXiv:1707.06386, 2017 | 96 | 2017 |

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 | 84 | 2019 |

Ring-LWE in polynomial rings L Ducas, A Durmus International Workshop on Public Key Cryptography, 34-51, 2012 | 73 | 2012 |

Irreducibility and geometric ergodicity of Hamiltonian Monte Carlo A Durmus, É Moulines, E Saksman The Annals of Statistics 48 (6), 3545-3564, 2020 | 60* | 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 | 46 | 2019 |

Sampling from a strongly log-concave distribution with the Unadjusted Langevin Algorithm A Durmus, E Moulines | 44 | 2016 |

The tamed unadjusted Langevin algorithm N Brosse, A Durmus, É Moulines, S Sabanis Stochastic Processes and their Applications 129 (10), 3638-3663, 2019 | 41 | 2019 |

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 | 37 | 2017 |

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 | 36 | 2018 |

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 | 31 | 2020 |

Stochastic gradient richardson-romberg markov chain monte carlo A Durmus, U Simsekli, E Moulines, R Badeau, G Richard Thirtieth Annual Conference on Neural Information Processing Systems (NIPS), 2016 | 31 | 2016 |

Geometric ergodicity of the bouncy particle sampler A Durmus, A Guillin, P Monmarché The Annals of Applied Probability 30 (5), 2069-2098, 2020 | 29 | 2020 |

Hypocoercivity of piecewise deterministic Markov process-Monte Carlo C Andrieu, A Durmus, N Nüsken, J Roussel arXiv preprint arXiv:1808.08592, 2018 | 27 | 2018 |

Piecewise deterministic Markov processes and their invariant measures A Durmus, A Guillin, P Monmarché Annales de l'Institut Henri Poincaré, Probabilités et Statistiques 57 (3 …, 2021 | 25 | 2021 |

Subgeometric rates of convergence in Wasserstein distance for Markov chains A Durmus, G Fort, É Moulines Annales de l'Institut Henri Poincaré, Probabilités et Statistiques 52 (4 …, 2016 | 25 | 2016 |

Asymptotic guarantees for learning generative models with the sliced-wasserstein distance K Nadjahi, A Durmus, U Şimşekli, R Badeau arXiv preprint arXiv:1906.04516, 2019 | 22 | 2019 |