Adaptive Sensor Placement for Continuous Spaces. - James A. Grant; Alexis Boukouvalas; Ryan-Rhys Griffiths; David S. Leslie; Sattar Vakili; Enrique Munoz de Cote (ICML, 2019)

Achieving Robustness to Aleatoric Uncertainty with Heteroscedastic Bayesian Optimisation. - Ryan-Rhys Griffiths; Miguel Garcia-Ortegon; Alexander A. Aldrick; Alpha A. Lee (CoRR, 2019)

Gaussian Process Molecule Property Prediction with FlowMO. - Henry B. Moss; Ryan-Rhys Griffiths (CoRR, 2020)

Generative Model-Enhanced Human Motion Prediction. - Anthony Bourached; Ryan-Rhys Griffiths; Robert J. Gray; Ashwani Jha; Parashkev Nachev (CoRR, 2020)

Recovery of underdrawings and ghost-paintings via style transfer by deep convolutional neural networks: A digital tool for art scholars. - Anthony Bourached; George H. Cann; Ryan-Rhys Griffiths; David G. Stork (Computer Vision and Image Analysis of Art, 2021)

Resolution enhancement in the recovery of underdrawings via style transfer by generative adversarial deep neural networks. - George H. Cann; Anthony Bourached; Ryan-Rhys Griffiths; David G. Stork (Computer Vision and Image Analysis of Art, 2021)

Computational identification of significant actors in paintings through symbols and attributes. - David G. Stork; Anthony Bourached; George H. Cann; Ryan-Rhys Griffiths (Computer Vision and Image Analysis of Art, 2021)

Dataset Bias in the Natural Sciences: A Case Study in Chemical Reaction Prediction and Synthesis Design. - Ryan-Rhys Griffiths; Philippe Schwaller; Alpha A. Lee (CoRR, 2021)

High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning. - Antoine Grosnit; Rasul Tutunov; Alexandre Max Maraval; Ryan-Rhys Griffiths; Alexander I. Cowen-Rivers; Lin Yang; Lin Zhu; Wenlong Lyu; Zhitang Chen; Jun Wang 012; Jan Peters 001; Haitham Bou-Ammar (CoRR, 2021)

Data Considerations in Graph Representation Learning for Supply Chain Networks. - Ajmal Aziz; Edward Elson Kosasih; Ryan-Rhys Griffiths; Alexandra Brintrup (CoRR, 2021)

Hierarchical Graph-Convolutional Variational AutoEncoding for Generative Modelling of Human Motion. - Anthony Bourached; Robert J. Gray; Ryan-Rhys Griffiths; Ashwani Jha; Parashkev Nachev (CoRR, 2021)

Are We Forgetting about Compositional Optimisers in Bayesian Optimisation? - Antoine Grosnit; Alexander I. Cowen-Rivers; Rasul Tutunov; Ryan-Rhys Griffiths; Jun Wang 012; Haitham Bou-Ammar (J. Mach. Learn. Res., 2021)

Extracting associations and meanings of objects depicted in artworks through bi-modal deep networks. - Gregory Kell; Ryan-Rhys Griffiths; Anthony Bourached; David G. Stork (Computer Vision and Image Analysis of Art, 2022)

GAUCHE: A Library for Gaussian Processes in Chemistry. - Ryan-Rhys Griffiths; Leo Klarner; Henry B. Moss; Aditya Ravuri; Sang Truong; Bojana Rankovic; Yuanqi Du; Arian R. Jamasb; Julius Schwartz; Austin Tripp; Gregory Kell; Anthony Bourached; et al. (CoRR, 2022)

HEBO: An Empirical Study of Assumptions in Bayesian Optimisation. - Alexander I. Cowen-Rivers; Wenlong Lyu; Rasul Tutunov; Zhi Wang 001; Antoine Grosnit; Ryan-Rhys Griffiths; Alexandre Max Maraval; Jianye Hao; Jun Wang 012; Jan Peters 001; Haitham Bou-Ammar (J. Artif. Intell. Res., 2022)

Achieving robustness to aleatoric uncertainty with heteroscedastic Bayesian optimisation. - Ryan-Rhys Griffiths; Alexander A. Aldrick; Miguel Garcia-Ortegon; Vidhi Lalchand; Alpha A. Lee (Mach. Learn. Sci. Technol., 2022)

Mathematical Capabilities of ChatGPT. - Simon Frieder; Luca Pinchetti; Alexis Chevalier; Ryan-Rhys Griffiths; Tommaso Salvatori; Thomas Lukasiewicz; Philipp Petersen; Julius Berner (NeurIPS, 2023)

GAUCHE: A Library for Gaussian Processes in Chemistry. - Ryan-Rhys Griffiths; Leo Klarner; Henry B. Moss; Aditya Ravuri; Sang Truong; Yuanqi Du; Samuel Stanton; Gary Tom; Bojana Rankovic; Arian Rokkum Jamasb; Aryan Deshwal; Julius Schwartz; et al. (NeurIPS, 2023)

Applications of Gaussian Processes at Extreme Lengthscales: From Molecules to Black Holes. - Ryan-Rhys Griffiths (CoRR, 2023)

RealMedQA: A pilot biomedical question answering dataset containing realistic clinical questions. - Gregory Kell; Angus Roberts; Serge Umansky; Yuti Khare; Najma Ahmed; Nikhil Patel; Chloe Simela; Jack Coumbe; Julian Rozario; Ryan-Rhys Griffiths; Iain James Marshall (CoRR, 2024)

Data for Mathematical Copilots: Better Ways of Presenting Proofs for Machine Learning. - Simon Frieder; Jonas Bayer; Katherine M. Collins; Julius Berner; Jacob Loader; András Juhász; Fabian Ruehle; Sean Welleck; Gabriel Poesia; Ryan-Rhys Griffiths; Adrian Weller; Anirudh Goyal; et al. (CoRR, 2024)

Aviary: training language agents on challenging scientific tasks. - Siddharth Narayanan; James D. Braza; Ryan-Rhys Griffiths; Manvitha Ponnapati; Albert Bou; Jon M. Laurent; Ori Kabeli; Geemi P. Wellawatte; Sam Cox; Samuel G. Rodriques; Andrew D. White (CoRR, 2024)

Analyzing global utilization and missed opportunities in debt-for-nature swaps with generative AI. - Nataliya Tkachenko; Simon Frieder; Ryan-Rhys Griffiths; Christoph Nedopil (Frontiers Artif. Intell., 2024)

Training a Scientific Reasoning Model for Chemistry. - Siddharth Narayanan; James D. Braza; Ryan-Rhys Griffiths; Albert Bou; Geemi P. Wellawatte; Mayk Caldas Ramos; Ludovico Mitchener; Samuel G. Rodriques; Andrew D. White (CoRR, 2025)