Alain Durmus
Alain Durmus
ENS Paris-Saclay
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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
Ring-LWE in polynomial rings
L Ducas, A Durmus
International Workshop on Public Key Cryptography, 34-51, 2012
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
J. Mach. Learn. Res. 20, 73:1-73:46, 2019
On the convergence of hamiltonian monte carlo
A Durmus, E Moulines, E Saksman
arXiv preprint arXiv:1705.00166, 2017
Sampling from strongly log-concave distributions with the Unadjusted Langevin Algorithm
A Durmus, E Moulines
arXiv preprint arXiv:1605.01559 5, 2016
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
arXiv preprint arXiv:1705.08964, 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
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
An elementary approach to uniform in time propagation of chaos
A Durmus, A Eberle, A Guillin, R Zimmer
arXiv preprint arXiv:1805.11387, 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
The promises and pitfalls of stochastic gradient Langevin dynamics
N Brosse, A Durmus, E Moulines
Advances in Neural Information Processing Systems, 8268-8278, 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
Geometric ergodicity of the bouncy particle sampler
A Durmus, A Guillin, P Monmarché
arXiv preprint arXiv:1807.05401, 2018
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
Hypocoercivity of piecewise deterministic Markov process-Monte Carlo
C Andrieu, A Durmus, N Nüsken, J Roussel
arXiv preprint arXiv:1808.08592, 2018
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