488 citations · 3k across the 119 of their papers we have counts for
6 papers · 1 filter
Efficient Bayesian computation by proximal Markov chain Monte Carlo: when Langevin meets Moreau
Alain Durmus, Eric Moulines, Marcelo Pereyra
Modern imaging methods rely strongly on Bayesian inference techniques to solve challenging imaging problems. Currently, the predominant Bayesian computation approach is convex opti…
Decentralized Frank-Wolfe Algorithm for Convex and Non-convex Problems
Hoi-To Wai, Jean Lafond, Anna Scaglione +1
Decentralized optimization algorithms have received much attention due to the recent advances in network information processing. However, conventional decentralized algorithms base…
On the two-filter approximations of marginal smoothing distributions in general state space models
Thi Ngoc Minh Nguyen, Sylvain Le Corff, Eric Moulines
A prevalent problem in general state space models is the approximation of the smoothing distribution of a state conditional on the observations from the past, the present, and the…
High-dimensional Bayesian inference via the Unadjusted Langevin Algorithm
Alain Durmus, Eric Moulines
We consider in this paper the problem of sampling a high-dimensional probability distribution having a density with respect to the Lebesgue measure on , known up…
Optimal scaling of the Random Walk Metropolis algorithm under Lp mean differentiability
Alain Durmus, Sylvain Le Corff, Eric Moulines +1
This paper considers the optimal scaling problem for high-dimensional random walk Metropolis algorithms for densities which are differentiable in Lp mean but which may be irregular…
Online EM for Functional Data
Florian Maire, Eric Moulines, Sidonie Lefebvre
A novel approach to perform unsupervised sequential learning for functional data is proposed. Our goal is to extract reference shapes (referred to as templates) from noisy, deforme…