9 citations · 10 across the 7 of their papers we have counts for
9 papers
Cluster formation for weakly interacting kinetic Langevin dynamics
Benedict Leimkuhler, René Lohmann, Grigorios A. Pavliotis +1
In this paper, we study the formation of clusters for stochastic interacting particle systems (IPS) that interact through short-range attractive potentials in a periodic domain. We…
A Langevin sampling algorithm inspired by the Adam optimizer
Benedict Leimkuhler, René Lohmann, Peter Whalley
We present a framework for adaptive-stepsize MCMC sampling based on time-rescaled Langevin dynamics, in which the stepsize variation is dynamically driven by an additional degree o…
Sampling from Bayesian Neural Network Posteriors with Symmetric Minibatch Splitting Langevin Dynamics
Daniel Paulin, Peter A. Whalley, Neil K. Chada +1
We propose a scalable kinetic Langevin dynamics algorithm for sampling parameter spaces of big data and AI applications. Our scheme combines a symmetric forward/backward sweep over…
Convergence of kinetic Langevin samplers for non-convex potentials
Katharina Schuh, Peter A. Whalley
We study three kinetic Langevin samplers including the Euler discretization, the BU and the UBU splitting scheme. We provide contraction results in -Wasserstein distance for n…
Correction to "Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations"
Daniel Paulin, Peter A. Whalley
A method for analyzing non-asymptotic guarantees of numerical discretizations of ergodic SDEs in Wasserstein-2 distance is presented by Sanz-Serna and Zygalakis in ``Wasserstein di…
Unbiased Kinetic Langevin Monte Carlo with Inexact Gradients
Neil K. Chada, Benedict Leimkuhler, Daniel Paulin +1
We present an unbiased method for Bayesian posterior means based on kinetic Langevin dynamics that combines advanced splitting methods with enhanced gradient approximations. Our ap…