4 citations · 6 across the 2 of their papers we have counts for
4 papers
Stein Variational Gradient Descent: many-particle and long-time asymptotics
Nikolas Nüsken, D. R. Michiel Renger
Stein variational gradient descent (SVGD) refers to a class of methods for Bayesian inference based on interacting particle systems. In this paper, we consider the originally propo…
VarGrad: A Low-Variance Gradient Estimator for Variational Inference
Lorenz Richter, Ayman Boustati, Nikolas Nüsken +2
We analyse the properties of an unbiased gradient estimator of the ELBO for variational inference, based on the score function method with leave-one-out control variates. We show t…
Affine invariant interacting Langevin dynamics for Bayesian inference
Alfredo Garbuno-Inigo, Nikolas Nüsken, Sebastian Reich
We propose a computational method (with acronym ALDI) for sampling from a given target distribution based on first-order (overdamped) Langevin dynamics which satisfies the property…
Note on Interacting Langevin Diffusions: Gradient Structure and Ensemble Kalman Sampler by Garbuno-Inigo, Hoffmann, Li and Stuart
Nikolas Nüsken, Sebastian Reich
An interacting system of Langevin dynamics driven particles has been proposed for sampling from a given posterior density by Garbuno-Inigo, Hoffmann, Li and Stuart in Interacting L…