We are a machine learning research group based in the Department of Statistics at the University of Oxford. Our work centres on foundational and methodological problems in machine learning, with a particular focus on data-efficient approaches and intelligent data acquisition. Topics of research include deep learning, experimental design, Bayesian reasoning, representation learning, generative models, Monte Carlo methods, active learning, probabilistic programming, and variational inference.
Oct 2025 – ****Welcome to the lab Ole and Tom!
Sep 2025 – “Scaling Up Active Testing to Large Language Models” has been accepted at NeurIPS 2025. Congrats to Gabrielle and Jannik, who led this project.
May 2025 – Three RainML papers have been accepted at ICML 2025. Well done to Freddie, Gábor and Marcel for leading these projects.
Mar 2025 – Welcome to the lab Zhuoyue!
Feb 2025 – Welcome to the lab Gavin!
Oct 2024 – ****Welcome to the lab Adhi, Benjamin, Deepro and Rik!
May 2024 – ”Generative Flows on Discrete State-Spaces” has been accepted at ICML 2024. Congrats to Andrew, who led this project.
Feb 2024 – Welcome to the lab Kia!
Jan 2024 – Three RainML papers have been accepted at AISTATS 2024. Congrats to Freddie, Guneet and Tim, who led these projects.
Jan 2024 – Two RainML papers on large language models have been accepted at ICLR 2024. Well done to Jannik and Ning, the first authors on these papers.
Dec 2023 – Welcome to the lab Marcel!
Dec 2023 – ****Tom has been awarded an ERC Starting Grant titled “Data-Driven Algorithms for Data Acquisition”. The grant is due to start in February 2024.
Nov 2023 – Welcome to the lab Angus!
Sep 2023 – ****Two RainML papers have been accepted at NeurIPS 2023. Congrats to Andrew and Jin, who led the projects.
Aug 2023 – ****Welcome to the lab Alex!