19 citations · 75 across the 19 of their papers we have counts for
6 papers · 1 filter
On the geometric convergence for MALA under verifiable conditions
Alain Durmus, Éric Moulines
While the Metropolis Adjusted Langevin Algorithm (MALA) is a popular and widely used Markov chain Monte Carlo method, very few papers derive conditions that ensure its convergence.…
NEO: Non Equilibrium Sampling on the Orbit of a Deterministic Transform
Achille Thin, Yazid Janati, Sylvain Le Corff +5
Sampling from a complex distribution and approximating its intractable normalizing constant Z are challenging problems. In this paper, a novel family of importance samplers (IS…
Nonreversible MCMC from conditional invertible transforms: a complete recipe with convergence guarantees
Achille Thin, Nikita Kotelevskii, Christophe Andrieu +3
Markov Chain Monte Carlo (MCMC) is a class of algorithms to sample complex and high-dimensional probability distributions. The Metropolis-Hastings (MH) algorithm, the workhorse of…
Approximate Bayesian Computation with the Sliced-Wasserstein Distance
Kimia Nadjahi, Valentin De Bortoli, Alain Durmus +2
Approximate Bayesian Computation (ABC) is a popular method for approximate inference in generative models with intractable but easy-to-sample likelihood. It constructs an approxima…
Efficient stochastic optimisation by unadjusted Langevin Monte Carlo. Application to maximum marginal likelihood and empirical Bayesian estimation
Valentin De Bortoli, Alain Durmus, Marcelo Pereyra +1
Stochastic approximation methods play a central role in maximum likelihood estimation problems involving intractable likelihood functions, such as marginal likelihoods arising in p…
Analysis of Langevin Monte Carlo via convex optimization
Alain Durmus, Szymon Majewski, Błażej Miasojedow
In this paper, we provide new insights on the Unadjusted Langevin Algorithm. We show that this method can be formulated as a first order optimization algorithm of an objective func…