23 citations · 52 across the 9 of their papers we have counts for
3 papers · 1 filter
Testing whether a Learning Procedure is Calibrated
Jon Cockayne, Matthew M. Graham, Chris J. Oates +2
A learning procedure takes as input a dataset and performs inference for the parameters of a model that is assumed to have given rise to the dataset. Here we consider learning…
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…
Manifold lifting: scaling MCMC to the vanishing noise regime
Khai Xiang Au, Matthew M. Graham, Alexandre H. Thiery
Standard Markov chain Monte Carlo methods struggle to explore distributions that are concentrated in the neighbourhood of low-dimensional structures. These pathologies naturally oc…