1 citations · 2 across the 5 of their papers we have counts for
5 papers
Adaptive Kernel Selection for Stein Variational Gradient Descent
Moritz Melcher, Simon Weissmann, Ashia C. Wilson +1
A central challenge in Bayesian inference is efficiently approximating posterior distributions. Stein Variational Gradient Descent (SVGD) is a popular variational inference method…
On the mean field limit of consensus based methods
Marvin Koß, Simon Weissmann, Jakob Zech
Consensus based optimization (CBO) employs a swarm of particles evolving as a system of stochastic differential equations (SDEs). Recently, it has been adapted to yield a derivativ…
Mathematical theory of deep learning
Philipp Petersen, Jakob Zech
This book provides an introduction to the mathematical analysis of deep learning. It covers fundamental results in approximation theory, optimization theory, and statistical learni…
On the mean-field limit for Stein variational gradient descent: stability and multilevel approximation
Simon Weissmann, Jakob Zech
In this paper we propose and analyze a novel multilevel version of Stein variational gradient descent (SVGD). SVGD is a recent particle based variational inference method. For Baye…
Metropolis-adjusted interacting particle sampling
Björn Sprungk, Simon Weissmann, Jakob Zech
In recent years, various interacting particle samplers have been developed to sample from complex target distributions, such as those found in Bayesian inverse problems. These samp…