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.
11 Feb 2025 – Welcome to the lab Gavin!
14 Oct 2024 – ****Welcome to the lab Adhi, Benjamin, Deepro and Rik!
21 Feb 2024 – Welcome to the lab Kia!
20 Jan 2024 – Three papers have been accepted at AISTATS 2024. Congrats to Freddie, Guneet and Tim, who led these projects.
16 Jan 2024 – Two papers on large language models have been accepted at ICLR 2024. Well done to Jannik and Ning, the first authors on these papers.
13 Dec 2023 – Welcome to the lab Marcel!
01 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.
01 Nov 2023 – Welcome to the lab Angus!
21 Sep 2023 – ****Two papers have been accepted at NeurIPS 2023. Congrats to Andrew and Jin, who led the projects.
23 Aug 2023 – ****Welcome to the lab Alex!
05 May 2023 – Our review paper on “Modern Bayesian Experimental Design” has been accepted for publication in Statistical Science. A preprint is available here.
24 Apr 2023 – Two papers have been accepted at ICML 2023. Congrats to Desi and Ning, who led the projects.
17 Feb 2023 – Two papers have been accepted at AISTATS 2023, with one for oral presentation. Well done to Freddie and Mrinank for leading these projects.
10 Oct 2022 – ****Welcome to the lab Shahine!
14 Sep 2022 – ****Three papers have been accepted at NeurIPS 2022, with two as oral presentations. Hats off to the RainML students who led these projects: Andrew, Jannik and Tim.