Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space. - José Miguel Hernández-Lobato; James Requeima; Edward O. Pyzer-Knapp; Alán Aspuru-Guzik (ICML, 2017)

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

Fast and Flexible Multi-Task Classification using Conditional Neural Adaptive Processes. - James Requeima; Jonathan Gordon 003; John Bronskill; Sebastian Nowozin; Richard E. Turner (NeurIPS, 2019)

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

TaskNorm: Rethinking Batch Normalization for Meta-Learning. - John Bronskill; Jonathan Gordon 003; James Requeima; Sebastian Nowozin; 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)

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)

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

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

Sim2Real for Environmental Neural Processes. - Jonas Scholz; Tom R. Andersson; Anna Vaughan; James Requeima; Richard E. Turner (CoRR, 2023)

Diffusion-Augmented Neural Processes. - Lorenzo Bonito; James Requeima; Aliaksandra Shysheya; Richard E. Turner (CoRR, 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)

LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language. - James Requeima; John Bronskill; Dami Choi; Richard E. Turner; David Kristjanson Duvenaud (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)

AI for operational methane emitter monitoring from space. - Anna Vaughan; Gonzalo Mateo-Garcia; Itziar Irakulis-Loitxate; Marc Watine; Pablo Fernandez-Poblaciones; Richard E. Turner; James Requeima; Javier Gorroño; Cynthia Randles; Manfredi Caltagirone; Claudio Cifarelli (CoRR, 2024)

Context is Key: A Benchmark for Forecasting with Essential Textual Information. - Andrew Robert Williams; Arjun Ashok; Étienne Marcotte; Valentina Zantedeschi; Jithendaraa Subramanian; Roland Riachi; James Requeima; Alexandre Lacoste; Irina Rish; Nicolas Chapados; Alexandre Drouin (CoRR, 2024)

A Meta-Learning Approach to Bayesian Causal Discovery. - Anish Dhir; Matthew Ashman; James Requeima; Mark van der Wilk (ICLR, 2025)

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)

JoLT: Joint Probabilistic Predictions on Tabular Data Using LLMs. - Aliaksandra Shysheya; John Bronskill; James Requeima; Shoaib Ahmed Siddiqui; Javier Gonzalez; David Duvenaud; Richard E. Turner (CoRR, 2025)

Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning. - Anish Dhir; Cristiana Diaconu; Valentinian Mihai Lungu; James Requeima; Richard E. Turner; Mark van der Wilk (CoRR, 2025)