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We work on some of the most complex and interesting challenges in AI.

Explore our research across
Deep learning
Theory and foundations
Control and robotics
Unsupervised learning and generative models
Reinforcement learning
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Highlighted research

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Visual representation of AlphaFold.
AlphaFold
AlphaFold can accurately predict 3D models of protein structures and has the potential to accelerate research in every field of biology.
Find out more
Visual representation of WaveNet generating system voices on devices.
WaveNet
WaveNet creates more natural-sounding speech for products used by millions of people around the world.
Find out more

Featured publications

Visual representation of AlphaFold.
Restoring and attributing ancient texts using deep neural networks
Restoring, placing, and dating ancient texts through collaboration between AI and historians.
Nature
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Visual representation of WaveNet generating system voices on devices.
Magnetic control of tokamak plasmas through deep reinforcement learning
Successfully controlling the nuclear fusion plasma in a tokamak with deep reinforcement learning.
Nature
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Visual representation of WaveNet generating system voices on devices.
Advancing mathematics by guiding human intuition with AI
Applying AI toward discovering new insights in two areas of pure mathematics: topology and representation theory.
Nature
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Publications

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Learning to Configure Computer Networks with Neural Algorithmic Reasoning
Luca Beurer-Kellner *, Martin Vechev *, Laurent Vanbever *, Petar Veličković
NeurIPS
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Latent Space Smoothing for Individually Fair Representations
Momchil Peychev *, Anian Ruoss, Mislav Balunovic *, Maximilian Baader *, Martin Vechev *
ECCV
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Testing Independence of Exchangeable Random Variables
Marcus Hutter
arXiv
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Why neural networks find simple solutions: the many regularizers of geometric complexity
Benoit Dherin *, Michael Munn *, David Barrett, Mihaela Rosca
NeurIPS
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Self-supervised video pretraining yields strong image representations
Nikhil Parthasarathy, Ali Eslami, Joao Carreira, Olivier Henaff
arXiv
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Beyond Bayes-optimality: meta-learning what you know you don’t know
Jordi Grau, Grégoire Delétang, Markus Kunesch, Tim Genewein, Kevin Li, Elliot Catt, Anian Ruoss, Chris Cundy, Joel Veness, Jane Wang, Marcus Hutter, Christopher Summerfield, Shane Legg
arXiv
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Transformers generalize differently from in-context and in-weights information
Stephanie Chan, Ishita Dasgupta, Junkyung Kim, Dharshan Kumaran, Andrew Lampinen, Felix Hill
arXiv
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Palm up: Playing in the Latent Manifold for Unsupervised Pretraining
Hao Liu *, Tom Zahavy, Vlad Mnih, Satinder Baveja
NeurIPS
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Game Theoretic Rating in n-player general-sum games with Equilibria
Luke Marris, Marc Lanctot, Ian Gemp, Shayegan Omidshafiei, Stephen Mcaleer, Jerome Connor, Karl Tuyls, Thore Graepel *
arXiv
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Optimistic posterior sampling for reinforcement learning with few samples and tight guarantees
Daniil Tiapkin *, Denis Belomestny *, Daniele Calandriello, Eric Moulines *, Alexey Naumov *, Remi Munos, Mark Rowland, Pierre Menard *, Michal Valko
NeurIPS
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Co-Writing Screenplays and Theatre Scripts with Language Models: An Evaluation by Industry Professionals
Piotr Mirowski, Kory Mathewson, Jaylen Pittman, Richard Evans
arXiv
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Where Should I Spend My FLOPS? Efficiency Evaluations of Visual Pre-training Methods
Skanda Koppula, Yazhe Li, Evan Shelhamer, Drew Jaegle, Nikhil Parthasarathy, Relja Arandjelovic, Joao Carreira, Olivier Henaff
NeurIPS Workshop
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COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation
Jongmin Lee *, Cosmin Paduraru, Daniel J. Mankowitz, Nicolas Heess, Doina Precup, Kee-Eung Kim *, Arthur Guez
ICLR
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Discovering Policies with DOMiNO: Diversity Optimization Maintaining Near Optimality
Tom Zahavy, Feryal Behbahani, Yannick Schroecker, Kate Baumli, Sebastian Flennerhag, Shaobo Hou, Satinder Baveja
European Workshop on Reinforcement Learning (EWRL)
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Structure of the PAPP-A-IGFBP5 complex reveals mechanism of substrate recognition
Russell A. Judge *, Janani Sridar *, Kathryn Tunyasuvunakool, Rinku Jain *, John C.K. Wang *, Ouch Christna *, John Jumper, Jun Xu *, Amirhossein Mafi *, Aaron H. Nile *, Clint Remarcik *, Corey L. Smith *, Crystal Ghosh *, Chen Xu *, Vincent Stoll *, Amoolya H. Singh *, Dan Eaton *, Qi Hao *
Nature Communications
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On Reward Binarisation and Bayesian Agents
Elliot Catt, Marcus Hutter, Joel Veness
European Workshop on Reinforcement Learning (EWRL)
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Optimizing Industrial Cooling Systems with Hierarchical Reinforcement Learning
William Wong *, Praneet Dutta, Jerry Luo, Octavian Voicu, Yuri Chervonyi, Cosmin Paduraru
arXiv
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Developing, evaluating and scaling learning agents in multi-agent environments
Ian Gemp, Yoram Bachrach, Siqi Liu, Marc Lanctot, Karl Tuyls, Thomas Anthony, Kalesha Bullard, Avishkar Bhoopchand, Jerome Connor, Vibhavari Dasagi, Bart De Vylder, Edgar Duéñez-Guzmán, Romuald Elie, Richard Everett, Daniel Hennes, Edward Hughes, Guy Lever, Mina Khan, Kate Larson, Luke Marris, Kevin McKee, Paul Muller, Julien Perolat, Florian Strub, Andrea Tacchetti, Zhe Wang, Eugene Tarassov
AI Communications
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From Motor Control to Team Play in Simulated Humanoid Football
Siqi Liu, Guy Lever, Zhe Wang, Ali Eslami, Daniel Hennes, Wojtek Czarnecki, Yuval Tassa, Abbas Abdolmaleki, Noah Siegel, Leonard Hasenclever, Luke Marris, Saran Tunyasuvunakool, Francis Song, Markus Wulfmeier, Paul Muller, Tuomas Haarnoja, Brendan Tracey, Karl Tuyls
Science Robotics
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Of Correlated Equilibria in Mean-Field Games
Paul Muller, Romuald Elie, Mark Rowland, Julien Perolat, Karl Tuyls, Georgios Piliouras *, Mathieu Lauriere *, Matthieu Geist *, Sarah Perrin *, Olivier Pietquin *
arXiv
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Probing Transfer in Deep Reinforcement Learning without Task Engineering
Andrei Rusu, Sebastian Flennerhag, Dushyant Rao, Razvan Pascanu, Raia Hadsell
CoLLAs
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Reinforcement Learning with Information Theoretic Actuation
Elliot Catt, Marcus Hutter, Joel Veness
arXiv
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Meta-Learning Sparse Compression Networks
Yee Whye Teh, Jonathan Schwarz
Transactions on Machine Learning Research (TMLR)
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Paper
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How fair is your graph? Addressing fairness concerns in neuroimaging studies
Ira Ktena, Fernanda Ribeiro *, Valentina Shumovskaia *, Thomas Davies *
Machine Learning for Healthcare 2022
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Paper
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Object discovery and representation networks
Olivier Henaff, Skanda Koppula, Evan Shelhamer, Daniel Zoran, Drew Jaegle, Andrew Zisserman, Joao Carreira, Relja Arandjelovic
ECCV
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