| Lattice signatures and bimodal Gaussians L Ducas, A Durmus, T Lepoint, V Lyubashevsky Annual Cryptology Conference, 40-56, 2013 | 508 | 2013 |
| Nonasymptotic convergence analysis for the unadjusted Langevin algorithm A Durmus, E Moulines Annals of Applied Probability 27 (3), 1551-1587, 2017 | 192 | 2017 |
| High-dimensional Bayesian inference via the unadjusted Langevin algorithm A Durmus, E Moulines Bernoulli 25 (4A), 2854-2882, 2019 | 122 | 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 | 93 | 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 | 77 | 2017 |
| Ring-LWE in polynomial rings L Ducas, A Durmus International Workshop on Public Key Cryptography, 34-51, 2012 | 66 | 2012 |
| 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 | 65 | 2019 |
| On the convergence of hamiltonian monte carlo A Durmus, E Moulines, E Saksman arXiv preprint arXiv:1705.00166, 2017 | 48 | 2017 |
| Sampling from a strongly log-concave distribution with the Unadjusted Langevin Algorithm A Durmus, E Moulines | 40 | 2016 |
| 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 | 38 | 2019 |
| The tamed unadjusted Langevin algorithm N Brosse, A Durmus, É Moulines, S Sabanis Stochastic Processes and their Applications 129 (10), 3638-3663, 2019 | 33 | 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 | 31 | 2017 |
| 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 | 27 | 2016 |
| The promises and pitfalls of stochastic gradient Langevin dynamics N Brosse, A Durmus, E Moulines arXiv preprint arXiv:1811.10072, 2018 | 26 | 2018 |
| Geometric ergodicity of the bouncy particle sampler A Durmus, A Guillin, P Monmarché Annals of Applied Probability 30 (5), 2069-2098, 2020 | 23 | 2020 |
| Piecewise Deterministic Markov Processes and their invariant measure A Durmus, A Guillin, P Monmarché arXiv preprint arXiv:1807.05421, 2018 | 23 | 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 | 22 | 2020 |
| 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 | 22 | 2016 |
| Hypocoercivity of piecewise deterministic Markov process-Monte Carlo C Andrieu, A Durmus, N Nüsken, J Roussel arXiv preprint arXiv:1808.08592, 2018 | 21 | 2018 |
| 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 | 20 | 2019 |
