Publications

We do research in both theoretical machine learning, as well as applications of machine learning in various domains, including medical, astronomy, autonomous driving, and more.

Recent publications:

	08/2021: "Provable Guarantees on the Robustness of Decision Rules to Causal Interventions" (Benjie Wang, Clare Lyle, Marta Kwiatkowska).
	07/2021: "Improving black-box optimization in VAE latent space using decoder uncertainty" (Pascal Notin, José Miguel Hernández-Lobato, Yarin Gal).
	05/2021: "Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces" (Xingchen Wan, Vu Nguyen, Huong Ha, Binxin (Robin) Ru, Cong Lu, Michael A. Osborne).

Reproducibility and Code

One of our lab's missions is to contribute to the reproducibility effort. Here we provide code for our own research, as well as reproductions of works by others (e.g. ones we implemented as baselines as part of our own research).

Recent code release:

	07/2020: OATomobile: A research framework for autonomous driving (Angelos Filos, Panagiotis Tigas).
	07/2020: Slurm for Machine Learning (Joost van Amersfoort).
	05/2020: Torch Memory-adaptive Algorithms (TOMA) (Andreas Kirsch).

Group Blog

Recent group blog posts:

	06 Aug 2021: Introducing the Shifts Challenge (Neil Band, Andrey Malinin, Panagiotis Tigas, Yarin Gal)
	17 Jul 2021: 21 OATML Conference and Workshop papers at ICML 2021 (Angelos Filos, Clare Lyle, Jannik Kossen, Sebastian Farquhar, Tom Rainforth, Andrew Jesson, Sören Mindermann, Tim G. J. Rudner, Oscar Key, Binxin (Robin) Ru, Pascal Notin, Panagiotis Tigas, Andreas Kirsch, Jishnu Mukhoti, Joost van Amersfoort, Lisa Schut, Muhammed Razzak, Aidan Gomez, Jan Brauner, Yarin Gal)
	27 Jun 2021: Why I Wouldn't Trust OpenAI's CLIP to Drive My Car (Yarin Gal)
	08 Dec 2020: When causal inference fails - detecting violated assumptions with uncertainty-aware models (Andrew Jesson, Sören Mindermann, Uri Shalit, Yarin Gal)

Group Members

We are researchers coming from varied backgrounds, including Computer Science, Maths & Stats, Engineering, and Physics.

We come from academia (Oxford, Cambridge, MILA, Manchester, U of Amsterdam, U of Toronto, U of Cape Town, Yale, and others) and industry (Google, DeepMind, Twitter, Qualcomm, and startups).

We include 5 Rhodes Scholars, 3 Clarendon Scholars, 3 DeepMind Scholars, one cancer research UK Scholar, with our students funded by many additional sources (AIMS CDT, Cyber CDT, industry, and more). If you'd like to join us take a look here.

Current group members: Yarin Gal (Associate Professor) ● Freddie Kalaitzis (Senior Research Fellow, 2020) ● Sebastian Farquhar (Postdoc, 2017) ● Jan Brauner (PhD, 2020) ● Jannik Kossen (PhD, 2020) ● Lisa Schut (PhD, 2020) ● Muhammed Razzak (PhD, 2020) ● Tuan Nguyen (Associate Member (PhD), 2020) ● Gabriel Jones (Associate Member (PhD), 2020) ● Atılım Güneş Baydin (Associate Member (Faculty), 2020) ● Mizu Nishikawa-Toomey (Research Assistant, 2020) ● Neil Band (MSc by Research, 2020) ● Andrew Jesson (PhD, 2019) ● Jishnu Mukhoti (PhD, 2019) ● Pascal Notin (PhD, 2019) ● Panagiotis Tigas (PhD, 2019) ● Sören Mindermann (PhD, 2019) ● Tom Rainforth (Associate Member (Faculty), 2019) ● Angelos Filos (PhD, 2018) ● Aidan Gomez (PhD, 2018) ● Andreas Kirsch (PhD, 2018) ● Clare Lyle (PhD, 2018) ● Joost van Amersfoort (PhD, 2018) ● Binxin (Robin) Ru (PhD, 2018) ● Tim G. J. Rudner (PhD, 2018) ● Lewis Smith (PhD, 2017) ● Milad Alizadeh (PhD, 2017)

Group Invited Talks

Recent invited talks:

16 Mar 2021, 15:00, Zoom	Debbie Marks (Harvard).
09 Mar 2021, 15:00, Zoom	Greg Yang (Microsoft Research). Feature Learning in Infinite-Width Neural Networks
02 Mar 2021, 14:00, Zoom	Uri Shalit (Technion - Israel Institute of Technology). Causality-Inspired Machine Learning
23 Feb 2021, 17:00, Zoom	Adi Hanuka (SLAC National Laboratory, Stanford). Machine learning for Design and Control of Particle Accelerators

Group Collaborators and Affiliates

Our group collaborators include:

And our group affiliates include: