Alain Durmus
Alain Durmus
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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
High-dimensional Bayesian inference via the unadjusted Langevin algorithm
A Durmus, E Moulines
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
Analysis of Langevin Monte Carlo via convex optimization
A Durmus, S Majewski, B Miasojedow
Journal of Machine Learning Research 20 (73), 1-46, 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
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
Ring-LWE in polynomial rings
L Ducas, A Durmus
Public Key Cryptography–PKC 2012: 15th International Conference on Practice …, 2012
Irreducibility and geometric ergodicity of Hamiltonian Monte Carlo
A Durmus, É Moulines, E Saksman
The Annals of Statistics 48 (6), 3545-3564, 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
Bayesian imaging using plug & play priors: when langevin meets tweedie
R Laumont, VD Bortoli, A Almansa, J Delon, A Durmus, M Pereyra
SIAM Journal on Imaging Sciences 15 (2), 701-737, 2022
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
The tamed unadjusted Langevin algorithm
N Brosse, A Durmus, É Moulines, S Sabanis
Stochastic Processes and their Applications 129 (10), 3638-3663, 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
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
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
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
Geometric ergodicity of the bouncy particle sampler
A Durmus, A Guillin, P Monmarché
The Annals of Applied Probability 30 (5), 2069-2098, 2020
Maximum likelihood estimation of regularization parameters in high-dimensional inverse problems: An empirical bayesian approach part i: Methodology and experiments
AF Vidal, V De Bortoli, M Pereyra, A Durmus
SIAM Journal on Imaging Sciences 13 (4), 1945-1989, 2020
Sampling from strongly log-concave distributions with the Unadjusted Langevin Algorithm
A Durmus, E Moulines
arXiv preprint arXiv:1605.01559, 2016
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