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.
23 Aug 2023 Welcome to the lab Alex!
05 May 2023 Our review paper on “Modern Bayesian Experimental Design” has been accepted to 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 Anya and 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.
01 Sep 2022 Applications are open for the 2023 cohorts of the DPhil in Statistics, the StatML CDT and the AIMS CDT. Feel free to reach out if you’re interested in doing a DPhil in the lab.
16 May 2022 “Expectation Programming” has been accepted for an oral presentation at UAI 2022. Congrats to Tim, who led this project.
29 Jan 2022 Two papers have been accepted at AISTATS 2022, with one for oral presentation.
24 Jan 2022 Two papers have been accepted at ICLR 2022, including one as a spotlight. “On Incorporating Inductive Biases into VAEs” was led by Ning.