4 citations · 6 across the 2 of their papers we have counts for
3 papers · 1 filter
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…
Solving high-dimensional parabolic PDEs using the tensor train format
Lorenz Richter, Leon Sallandt, Nikolas Nüsken
High-dimensional partial differential equations (PDEs) are ubiquitous in economics, science and engineering. However, their numerical treatment poses formidable challenges since tr…
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…