88 citations · 487 across the 78 of their papers we have counts for
6 papers · 1 filter
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(…
Bridging the Gap between Constant Step Size Stochastic Gradient Descent and Markov Chains
Aymeric Dieuleveut, Alain Durmus, Francis Bach
We consider the minimization of an objective function given access to unbiased estimates of its gradient through stochastic gradient descent (SGD) with constant step-size. While th…
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…
On the convergence of Hamiltonian Monte Carlo
Alain Durmus, Eric Moulines, Eero Saksman
This paper discusses the irreducibility and geometric ergodicity of the Hamiltonian Monte Carlo (HMC) algorithm. We consider cases where the number of steps of the symplectic integ…
Forward Event-Chain Monte Carlo: Fast sampling by randomness control in irreversible Markov chains
Manon Michel, Alain Durmus, Stéphane Sénécal
Irreversible and rejection-free Monte Carlo methods, recently developed in Physics under the name Event-Chain and known in Statistics as Piecewise Deterministic Monte Carlo (PDMC),…