88 citations · 487 across the 78 of their papers we have counts for
13 papers · 1 filter
On the convergence of dynamic implementations of Hamiltonian Monte Carlo and No U-Turn Samplers
Alain Durmus, Samuel Gruffaz, Miika Kailas +2
There is substantial empirical evidence about the success of dynamic implementations of Hamiltonian Monte Carlo (HMC), such as the No U-Turn Sampler (NUTS), in many challenging inf…
Optimal Scaling Results for Moreau-Yosida Metropolis-adjusted Langevin Algorithms
Francesca R. Crucinio, Alain Durmus, Pablo Jiménez +1
We consider a recently proposed class of MCMC methods which uses proximity maps instead of gradients to build proposal mechanisms which can be employed for both differentiable and…
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.…
Boost your favorite Markov Chain Monte Carlo sampler using Kac's theorem: the Kick-Kac teleportation algorithm
Randal Douc, Alain Durmus, Aurélien Enfroy +1
The present paper focuses on the problem of sampling from a given target distribution defined on some general state space. To this end, we introduce a novel class of non-revers…
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…
Hypocoercivity of Piecewise Deterministic Markov Process-Monte Carlo
Christophe Andrieu, Alain Durmus, Nikolas Nüsken +1
In this work, we establish -exponential convergence for a broad class of Piecewise Deterministic Markov Processes recently proposed in the context of Markov Process M…
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…
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),…
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…