120 citations · 568 across the 33 of their papers we have counts for
4 papers · 1 filter
Grassmann Stein Variational Gradient Descent
Xing Liu, Harrison Zhu, Jean-François Ton +2
Stein variational gradient descent (SVGD) is a deterministic particle inference algorithm that provides an efficient alternative to Markov chain Monte Carlo. However, SVGD has been…
Scalable Control Variates for Monte Carlo Methods via Stochastic Optimization
Shijing Si, Chris. J. Oates, Andrew B. Duncan +2
Control variates are a well-established tool to reduce the variance of Monte Carlo estimators. However, for large-scale problems including high-dimensional and large-sample setting…
On the geometry of Stein variational gradient descent
A. Duncan, N. Nuesken, L. Szpruch
Bayesian inference problems require sampling or approximating high-dimensional probability distributions. The focus of this paper is on the recently introduced Stein variational gr…
Measuring Sample Quality with Diffusions
Jackson Gorham, Andrew B. Duncan, Sebastian J. Vollmer +1
Stein's method for measuring convergence to a continuous target distribution relies on an operator characterizing the target and Stein factor bounds on the solutions of an associat…