Scalable Variational Gaussian Process Classification. - James Hensman; Alexander G. de G. Matthews; Zoubin Ghahramani (AISTATS, 2015)

MCMC for Variationally Sparse Gaussian Processes. - James Hensman; Alexander G. de G. Matthews; Maurizio Filippone; Zoubin Ghahramani (NIPS, 2015)

On Sparse Variational Methods and the Kullback-Leibler Divergence between Stochastic Processes. - Alexander G. de G. Matthews; James Hensman; Richard E. Turner; Zoubin Ghahramani (AISTATS, 2016)

GPflow: A Gaussian Process Library using TensorFlow. - Alexander G. de G. Matthews; Mark van der Wilk; Tom Nickson; Keisuke Fujii 002; Alexis Boukouvalas; Pablo León-Villagrá; Zoubin Ghahramani; James Hensman (J. Mach. Learn. Res., 2017)

Gaussian Process Behaviour in Wide Deep Neural Networks. - Alexander G. de G. Matthews; Jiri Hron; Mark Rowland 001; Richard E. Turner; Zoubin Ghahramani (ICLR (Poster), 2018)

Variational Bayesian dropout: pitfalls and fixes. - Jiri Hron; Alexander G. de G. Matthews; Zoubin Ghahramani (ICML, 2018)

Functional Regularisation for Continual Learning using Gaussian Processes. - Michalis K. Titsias; Jonathan Schwarz; Alexander G. de G. Matthews; Razvan Pascanu; Yee Whye Teh (CoRR, 2019)

Ab-Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Networks. - David Pfau; James S. Spencer; Alexander G. de G. Matthews; W. Matthew C. Foulkes (CoRR, 2019)

Functional Regularisation for Continual Learning with Gaussian Processes. - Michalis K. Titsias; Jonathan Schwarz; Alexander G. de G. Matthews; Razvan Pascanu; Yee Whye Teh (ICLR, 2020)

Annealed Flow Transport Monte Carlo. - Michael Arbel; Alexander G. de G. Matthews; Arnaud Doucet (ICML, 2021)

Continual Repeated Annealed Flow Transport Monte Carlo. - Alexander G. de G. Matthews; Michael Arbel; Danilo Jimenez Rezende; Arnaud Doucet (ICML, 2022)

Score-Based Diffusion meets Annealed Importance Sampling. - Arnaud Doucet; Will Grathwohl; Alexander G. de G. Matthews; Heiko Strathmann (NeurIPS, 2022)

Aspects of scaling and scalability for flow-based sampling of lattice QCD. - Ryan Abbott; Michael S. Albergo; Aleksandar Botev; Denis Boyda; Kyle Cranmer; Daniel C. Hackett; Alexander G. de G. Matthews; Sébastien Racanière; Ali Razavi; Danilo J. Rezende; Fernando Romero-López; Phiala E. Shanahan; et al. (CoRR, 2022)

Normalizing flows for lattice gauge theory in arbitrary space-time dimension. - Ryan Abbott; Michael S. Albergo; Aleksandar Botev; Denis Boyda; Kyle Cranmer; Daniel C. Hackett; Gurtej Kanwar; Alexander G. de G. Matthews; Sébastien Racanière; Ali Razavi; Danilo J. Rezende; Fernando Romero-López; et al. (CoRR, 2023)