88 citations · 487 across the 78 of their papers we have counts for
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DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs
Vincent Plassier, Maxime Vono, Alain Durmus +1
Performing reliable Bayesian inference on a big data scale is becoming a keystone in the modern era of machine learning. A workhorse class of methods to achieve this task are Marko…
Bayesian imaging using Plug & Play priors: when Langevin meets Tweedie
Rémi Laumont, Valentin de Bortoli, Andrés Almansa +3
Since the seminal work of Venkatakrishnan et al. in 2013, Plug & Play (PnP) methods have become ubiquitous in Bayesian imaging. These methods derive Minimum Mean Square Error (MMSE…
Maximum likelihood estimation of regularisation parameters in high-dimensional inverse problems: an empirical Bayesian approach. Part I: Methodology and Experiments
Ana F. Vidal, Valentin De Bortoli, Marcelo Pereyra +1
Many imaging problems require solving an inverse problem that is ill-conditioned or ill-posed. Imaging methods typically address this difficulty by regularising the estimation prob…
Diffusion approximations and control variates for MCMC
Nicolas Brosse, Alain Durmus, Sean Meyn +2
A new methodology is presented for the construction of control variates to reduce the variance of additive functionals of Markov Chain Monte Carlo (MCMC) samplers. Our control vari…
The Tamed Unadjusted Langevin Algorithm
Nicolas Brosse, Alain Durmus, Éric Moulines +1
In this article, we consider the problem of sampling from a probability measure having a density on known up to a normalizing constant, $x\mapsto \mathrm{e}^{-U(…
Normalizing constants of log-concave densities
Nicolas Brosse, Alain Durmus, Éric Moulines
We derive explicit bounds for the computation of normalizing constants for log-concave densities with respect to the Lebesgue measure on . Our app…
Sampling from a log-concave distribution with compact support with proximal Langevin Monte Carlo
Nicolas Brosse, Alain Durmus, Éric Moulines +1
This paper presents a detailed theoretical analysis of the Langevin Monte Carlo sampling algorithm recently introduced in Durmus et al. (Efficient Bayesian computation by proximal…