31 citations · 35 across the 10 of their papers we have counts for
6 papers · 1 filter
Harnessing the Power of Reinforcement Learning for Adaptive MCMC
Congye Wang, Matthew A. Fisher, Heishiro Kanagawa +2
Sampling algorithms drive probabilistic machine learning, and recent years have seen an explosion in the diversity of tools for this task. However, the increasing sophistication of…
Fast Approximate Solution of Stein Equations for Post-Processing of MCMC
Qingyang Liu, Heishiro Kanagawa, Matthew A. Fisher +2
Bayesian inference is conceptually elegant, but calculating posterior expectations can entail a heavy computational cost. Monte Carlo methods are reliable and supported by strong a…
Gradient-Free Kernel Stein Discrepancy
Matthew A Fisher, Chris. J Oates
Stein discrepancies have emerged as a powerful statistical tool, being applied to fundamental statistical problems including parameter inference, goodness-of-fit testing, and sampl…
GaussED: A Probabilistic Programming Language for Sequential Experimental Design
Matthew A. Fisher, Onur Teymur, Chris. J. Oates
Sequential algorithms are popular for experimental design, enabling emulation, optimisation and inference to be efficiently performed. For most of these applications bespoke softwa…
Measure Transport with Kernel Stein Discrepancy
Matthew A. Fisher, Tui Nolan, Matthew M. Graham +2
Measure transport underpins several recent algorithms for posterior approximation in the Bayesian context, wherein a transport map is sought to minimise the Kullback--Leibler diver…
A Locally Adaptive Bayesian Cubature Method
Matthew A Fisher, Chris J Oates, Catherine Powell +1
Bayesian cubature (BC) is a popular inferential perspective on the cubature of expensive integrands, wherein the integrand is emulated using a stochastic process model. Several app…