3 citations · 9 across the 4 of their papers we have counts for
6 papers
Adaptive Tuning for Metropolis Adjusted Langevin Trajectories
Lionel Riou-Durand, Pavel Sountsov, Jure Vogrinc +2
Hamiltonian Monte Carlo (HMC) is a widely used sampler for continuous probability distributions. In many cases, the underlying Hamiltonian dynamics exhibit a phenomenon of resonanc…
Metropolis Adjusted Langevin Trajectories: a robust alternative to Hamiltonian Monte Carlo
Lionel Riou-Durand, Jure Vogrinc
We introduce MALT: a new Metropolis adjusted sampler built upon the (kinetic) Langevin diffusion. Compared to Generalized Hamiltonian Monte Carlo (GHMC), the Metropolis correction…
Nested : Assessing the convergence of Markov chain Monte Carlo when running many short chains
Charles C. Margossian, Matthew D. Hoffman, Pavel Sountsov +3
Recent developments in parallel Markov chain Monte Carlo (MCMC) algorithms allow us to run thousands of chains almost as quickly as a single chain, using hardware accelerators such…
Bounding the error of discretized Langevin algorithms for non-strongly log-concave targets
Arnak S. Dalalyan, Avetik Karagulyan, Lionel Riou-Durand
In this paper, we provide non-asymptotic upper bounds on the error of sampling from a target density using three schemes of discretized Langevin diffusions. The first scheme is the…
On sampling from a log-concave density using kinetic Langevin diffusions
Arnak S. Dalalyan, Lionel Riou-Durand
Langevin diffusion processes and their discretizations are often used for sampling from a target density. The most convenient framework for assessing the quality of such a sampling…
Noise contrastive estimation: asymptotics, comparison with MC-MLE
Lionel Riou-Durand, Nicolas Chopin
A statistical model is said to be un-normalised when its likelihood function involves an intractable normalising constant. Two popular methods for parameter inference for these mod…