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:

	09/2019: "BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning" (Andreas Kirsch, Joost van Amersfoort, Yarin Gal).
	09/2019: "VIREL: A Variational Inference Framework for Reinforcement Learning" (Matthew Fellows, Anuj Mahajan, Tim G. J. Rudner, Shimon Whiteson).
	09/2019: "A Geometric Perspective on Optimal Representations for Reinforcement Learning" (Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taiga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle).

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:

	09/2019: Machine Learning Summer School (MLSS) Moscow: Bayesian Deep Learning 101 (Yarin Gal).
	07/2019: Putting TensorFlow back in PyTorch, back in TensorFlow (differentiable TensorFlow PyTorch adapters) (Andreas Kirsch).
	06/2019: Code for BatchBALD blog post and paper (active learning) (Andreas Kirsch, Joost van Amersfoort, Yarin Gal).

Group Blog

Recent group blog posts:

	02 Jul 2019: Poor generalization can be dangerous in RL! (Zac Kenton, Angelos Filos, Yarin Gal)
	24 Jun 2019: Human in the Loop: Deep Learning without Wasteful Labelling (Andreas Kirsch, Joost van Amersfoort, Yarin Gal)
	14 Jun 2019: Bayesian Deep Learning Benchmarks (Angelos Filos, Sebastian Farquhar, Aidan Gomez, Tim G. J. Rudner, Zac Kenton, Lewis Smith, Milad Alizadeh, Yarin Gal)
	05 Jun 2019: Targeted Dropout (Aidan Gomez)

Group Members

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

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

We include 3 Rhodes Scholars, 2 Clarendon Scholars, one DeepMind Scholar, with our students funded by many additional sources (AIMS CDT, Cyber CDT, and more).