GP-ALPS: Automatic Latent Process Selection for Multi-Output Gaussian Process Models. - Pavel Berkovich; Eric Perim; Wessel P. Bruinsma (AABI, 2019)

The Gaussian Process Autoregressive Regression Model (GPAR). - James Requeima; William Tebbutt; Wessel P. Bruinsma; Richard E. Turner (AISTATS, 2019)

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

Scalable Exact Inference in Multi-Output Gaussian Processes. - Wessel P. Bruinsma; Eric Perim; William Tebbutt; J. Scott Hosking; Arno Solin; Richard E. Turner (ICML, 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)

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)

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

Efficient Gaussian Neural Processes for Regression. - Stratis Markou; James Requeima; Wessel P. Bruinsma; Richard E. Turner (CoRR, 2021)

Modelling Non-Smooth Signals with Complex Spectral Structure. - Wessel P. Bruinsma; Martin Tegner; Richard E. Turner (AISTATS, 2022)

Wide Mean-Field Bayesian Neural Networks Ignore the Data. - Beau Coker; Wessel P. Bruinsma; David R. Burt; Weiwei Pan; Finale Doshi-Velez (AISTATS, 2022)

Practical Conditional Neural Process Via Tractable Dependent Predictions. - Stratis Markou; James Requeima; Wessel P. Bruinsma; Anna Vaughan; Richard E. Turner (ICLR, 2022)

Sparse Gaussian Process Hyperparameters: Optimize or Integrate? - Vidhi Lalchand; Wessel P. Bruinsma; David R. Burt; Carl Edward Rasmussen (NeurIPS, 2022)

Practical Conditional Neural Processes Via Tractable Dependent Predictions. - Stratis Markou; James Requeima; Wessel P. Bruinsma; Anna Vaughan; Richard E. Turner (CoRR, 2022)

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)

Challenges and Pitfalls of Bayesian Unlearning. - Ambrish Rawat; James Requeima; Wessel P. Bruinsma; Richard E. Turner (CoRR, 2022)

Active Learning with Convolutional Gaussian Neural Processes for Environmental Sensor Placement. - Tom R. Andersson; Wessel P. Bruinsma; Stratis Markou; James Requeima; Alejandro Coca-Castro; Anna Vaughan; Anna-Louise Ellis; Matthew Lazzara; Daniel C. Jones; J. Scott Hosking; Richard E. Turner (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)

Translation Equivariant Transformer Neural Processes. - Matthew Ashman; Cristiana Diaconu; Junhyuck Kim; Lakee Sivaraya; Stratis Markou; James Requeima; Wessel P. Bruinsma; Richard E. Turner (ICML, 2024)

Safe Exploration in Dose Finding Clinical Trials with Heterogeneous Participants. - Isabel Chien; Wessel P. Bruinsma; Javier González Hernández; Richard E. Turner (ICML, 2024)

Approximately Equivariant Neural Processes. - Matthew Ashman; Cristiana Diaconu; Adrian Weller; Wessel P. Bruinsma; Richard E. Turner (NeurIPS, 2024)

Noise-Aware Differentially Private Regression via Meta-Learning. - Ossi Räisä; Stratis Markou; Matthew Ashman; Wessel P. Bruinsma; Marlon Tobaben; Antti Honkela; Richard E. Turner (NeurIPS, 2024)

Aardvark Weather: end-to-end data-driven weather forecasting. - Anna Vaughan; Stratis Markou; Will Tebbutt; James Requeima; Wessel P. Bruinsma; Tom R. Andersson; Michael Herzog; Nicholas D. Lane; J. Scott Hosking; Richard E. Turner (CoRR, 2024)

Aurora: A Foundation Model of the Atmosphere. - Cristian Bodnar; Wessel P. Bruinsma; Ana Lucic; Megan Stanley; Johannes Brandstetter; Patrick Garvan; Maik Riechert; Jonathan A. Weyn; Haiyu Dong; Anna Vaughan; Jayesh K. Gupta; Kit Thambiratnam; et al. (CoRR, 2024)

Convolutional Conditional Neural Processes. - Wessel P. Bruinsma (CoRR, 2024)

End-to-end data-driven weather prediction. - Anna Allen; Stratis Markou; Will Tebbutt; James Requeima; Wessel P. Bruinsma; Tom R. Andersson; Michael Herzog; Nicholas D. Lane; Matthew Chantry; J. Scott Hosking; Richard E. Turner (Nat., 2025)

A foundation model for the Earth system. - Cristian Bodnar; Wessel P. Bruinsma; Ana Lucic; Megan Stanley; Anna Allen; Johannes Brandstetter; Patrick Garvan; Maik Riechert; Jonathan A. Weyn; Haiyu Dong; Jayesh K. Gupta; Kit Thambiratnam; et al. (Nat., 2025)

Accurate and scalable exchange-correlation with deep learning. - Giulia Luise; Chin-Wei Huang; Thijs Vogels; Derk P. Kooi; Sebastian Ehlert; Stephanie Lanius; Klaas J. H. Giesbertz; Amir Karton; Deniz Gunceler; Megan Stanley; Wessel P. Bruinsma; Lin Huang; et al. (CoRR, 2025)