Lattice signatures and bimodal Gaussians L Ducas, A Durmus, T Lepoint, V Lyubashevsky Annual Cryptology Conference, 40-56, 2013 | 460 | 2013 |

Nonasymptotic convergence analysis for the unadjusted Langevin algorithm A Durmus, E Moulines The Annals of Applied Probability 27 (3), 1551-1587, 2017 | 157 | 2017 |

High-dimensional Bayesian inference via the unadjusted Langevin algorithm A Durmus, E Moulines Bernoulli 25 (4A), 2854-2882, 2019 | 95 | 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 | 82 | 2018 |

Bridging the gap between constant step size stochastic gradient descent and markov chains A Dieuleveut, A Durmus, F Bach Annals of Statistics 48 (3), 1348-1382, 2020 | 63 | 2020 |

Ring-LWE in polynomial rings L Ducas, A Durmus International Workshop on Public Key Cryptography, 34-51, 2012 | 63 | 2012 |

Analysis of Langevin Monte Carlo via Convex Optimization. A Durmus, S Majewski, B Miasojedow J. Mach. Learn. Res. 20, 73:1-73:46, 2019 | 47 | 2019 |

On the convergence of hamiltonian monte carlo A Durmus, E Moulines, E Saksman arXiv preprint arXiv:1705.00166, 2017 | 36 | 2017 |

Sampling from strongly log-concave distributions with the Unadjusted Langevin Algorithm A Durmus, E Moulines arXiv preprint arXiv:1605.01559 5, 2016 | 35 | 2016 |

The tamed unadjusted Langevin algorithm N Brosse, A Durmus, É Moulines, S Sabanis Stochastic Processes and their Applications 129 (10), 3638-3663, 2019 | 32 | 2019 |

Sampling from a log-concave distribution with compact support with proximal Langevin Monte Carlo N Brosse, A Durmus, É Moulines, M Pereyra arXiv preprint arXiv:1705.08964, 2017 | 25 | 2017 |

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 | 24 | 2019 |

Stochastic gradient richardson-romberg markov chain monte carlo A Durmus, U Simsekli, E Moulines, R Badeau, G Richard Advances in Neural Information Processing Systems, 2047-2055, 2016 | 24 | 2016 |

An elementary approach to uniform in time propagation of chaos A Durmus, A Eberle, A Guillin, R Zimmer Proceedings of the American Mathematical Society, 2020 | 21 | 2020 |

The promises and pitfalls of stochastic gradient Langevin dynamics N Brosse, A Durmus, E Moulines Advances in Neural Information Processing Systems, 8268-8278, 2018 | 20 | 2018 |

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 | 20 | 2016 |

Geometric ergodicity of the bouncy particle sampler A Durmus, A Guillin, P Monmarché arXiv preprint arXiv:1807.05401, 2018 | 18 | 2018 |

Quantitative bounds of convergence for geometrically ergodic Markov chain in the Wasserstein distance with application to the Metropolis adjusted Langevin algorithm A Durmus, É Moulines Statistics and Computing 25 (1), 5-19, 2015 | 18 | 2015 |

Fast Langevin based algorithm for MCMC in high dimensions A Durmus, GO Roberts, G Vilmart, KC Zygalakis The Annals of Applied Probability 27 (4), 2195-2237, 2017 | 17 | 2017 |

Hypocoercivity of piecewise deterministic Markov process-Monte Carlo C Andrieu, A Durmus, N Nüsken, J Roussel arXiv preprint arXiv:1808.08592, 2018 | 16 | 2018 |