'In-Between' Uncertainty in Bayesian Neural Networks. - Andrew Y. K. Foong; Yingzhen Li; José Miguel Hernández-Lobato; Richard E. Turner (CoRR, 2019)

Pathologies of Factorised Gaussian and MC Dropout Posteriors in Bayesian Neural Networks. - Andrew Y. K. Foong; David R. Burt; Yingzhen Li; Richard E. Turner (CoRR, 2019)

Convolutional Conditional Neural Processes. - Jonathan Gordon 003; Wessel P. Bruinsma; Andrew Y. K. Foong; James Requeima; Yann Dubois; Richard E. Turner (ICLR, 2020)

Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes. - Andrew Y. K. Foong; Wessel P. Bruinsma; Jonathan Gordon 003; Yann Dubois; James Requeima; Richard E. Turner (NeurIPS, 2020)

On the Expressiveness of Approximate Inference in Bayesian Neural Networks. - Andrew Y. K. Foong; David R. Burt; Yingzhen Li; Richard E. Turner (NeurIPS, 2020)

Structured Weight Priors for Convolutional Neural Networks. - Tim Pearce; Andrew Y. K. Foong; Alexandra Brintrup (CoRR, 2020)

How Tight Can PAC-Bayes be in the Small Data Regime? - Andrew Y. K. Foong; Wessel P. Bruinsma; David R. Burt; Richard E. Turner (NeurIPS, 2021)

Collapsed Variational Bounds for Bayesian Neural Networks. - Marcin Tomczak; Siddharth Swaroop; Andrew Y. K. Foong; Richard E. Turner (NeurIPS, 2021)

Evaluating Approximate Inference in Bayesian Deep Learning. - Andrew Gordon Wilson; Pavel Izmailov; Matthew D. Hoffman; Yarin Gal; Yingzhen Li; Melanie F. Pradier; Sharad Vikram; Andrew Y. K. Foong; Sanae Lotfi; Sebastian Farquhar (NeurIPS (Competition and Demos), 2021)

The Gaussian Neural Process. - Wessel P. Bruinsma; James Requeima; Andrew Y. K. Foong; Jonathan Gordon 003; Richard E. Turner (CoRR, 2021)

A Note on the Chernoff Bound for Random Variables in the Unit Interval. - Andrew Y. K. Foong; Wessel P. Bruinsma; David R. Burt (CoRR, 2022)

Autoregressive Conditional Neural Processes. - Wessel P. Bruinsma; Stratis Markou; James Requeima; Andrew Y. K. Foong; Tom R. Andersson; Anna Vaughan; Anthony Buonomo; J. Scott Hosking; Richard E. Turner (ICLR, 2023)

Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics. - Leon Klein; Andrew Y. K. Foong; Tor Erlend Fjelde; Bruno Mlodozeniec; Marc Brockschmidt; Sebastian Nowozin; Frank Noé; Ryota Tomioka (NeurIPS, 2023)

Denoising Diffusion Probabilistic Models in Six Simple Steps. - Richard E. Turner; Cristiana-Diana Diaconu; Stratis Markou; Aliaksandra Shysheya; Andrew Y. K. Foong; Bruno Mlodozeniec (CoRR, 2024)

Improved motif-scaffolding with SE(3) flow matching. - Jason Yim; Andrew Campbell; Emile Mathieu; Andrew Y. K. Foong; Michael Gastegger; José Jiménez-Luna; Sarah Lewis; Victor Garcia Satorras; Bastiaan S. Veeling; Frank Noé; Regina Barzilay; Tommi S. Jaakkola (Trans. Mach. Learn. Res., 2024)