31 citations · 35 across the 10 of their papers we have counts for
10 papers
Overcoming Model Misspecification in Bayesian Inference of Molecular Signalling Networks
Yinghan Li, Heishiro Kanagawa, Francesca Romana Crucinio +2
Bayesian inference of molecular signalling networks usually relies on tractability of the marginal likelihood, enabling the set of possible networks to be efficiently explored. As…
A Distributional Optimisation Perspective on Combining Models in Deep Learning
Congye Wang, Yan Lin, Zheyang Shen +2
Combining predictions from different models can improve performance at machine learning tasks, but the training of the individual models and the rule used to combine them are typic…
Detecting Model Misspecification in Bayesian Inverse Problems via Variational Gradient Descent
Qingyang Liu, Matthew A. Fisher, Zheyang Shen +4
Bayesian inference is optimal when the statistical model is well-specified, while outside this setting Bayesian inference can catastrophically fail; accordingly a wealth of post-Ba…
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…
Decodable hybrid dynamics of open quantum systems with Z_2 symmetry
Yaodong Li, Matthew P. A. Fisher
We explore a class of "open" quantum circuit models with local decoherence ("noise") and local projective measurements, each respecting a global Z_2 symmetry. The model supports a…
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…