Alain Durmus
Alain Durmus
ENS Paris-Saclay
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Cited by
Cited by
Lattice signatures and bimodal Gaussians
L Ducas, A Durmus, T Lepoint, V Lyubashevsky
Annual Cryptology Conference, 40-56, 2013
Nonasymptotic convergence analysis for the unadjusted Langevin algorithm
A Durmus, E Moulines
The Annals of Applied Probability 27 (3), 1551-1587, 2017
High-dimensional Bayesian inference via the unadjusted Langevin algorithm
A Durmus, E Moulines
Bernoulli 25 (4A), 2854-2882, 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
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
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
Ring-LWE in polynomial rings
L Ducas, A Durmus
International Workshop on Public Key Cryptography, 34-51, 2012
Irreducibility and geometric ergodicity of Hamiltonian Monte Carlo
A Durmus, É Moulines, E Saksman
The Annals of Statistics 48 (6), 3545-3564, 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
Sampling from a strongly log-concave distribution with the Unadjusted Langevin Algorithm
A Durmus, E Moulines
The tamed unadjusted Langevin algorithm
N Brosse, A Durmus, É Moulines, S Sabanis
Stochastic Processes and their Applications 129 (10), 3638-3663, 2019
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
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
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
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
Geometric ergodicity of the bouncy particle sampler
A Durmus, A Guillin, P Monmarché
The Annals of Applied Probability 30 (5), 2069-2098, 2020
Hypocoercivity of piecewise deterministic Markov process-Monte Carlo
C Andrieu, A Durmus, N Nüsken, J Roussel
arXiv preprint arXiv:1808.08592, 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
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
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
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