Research

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

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Deep learning

Theory and foundations

Control and robotics

Unsupervised learning and generative models

Reinforcement learning

Neuroscience

Sciences

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AlphaFold

AlphaFold can accurately predict 3D models of protein structures and has the potential to accelerate research in every field of biology.

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WaveNet

WaveNet creates more natural-sounding speech for products used by millions of people around the world.

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Featured publications

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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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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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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Publication

Meta-Learning Black-Box Optimization via Black-Box Optimization

Robert Lange, Tom Schaul, Tom Zahavy, Yutian Chen, Valentin Dalibard, Chris Lu, Satinder Baveja, Sebastian Flennerhag

ICLR

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Is forgetting less a good inductive bias for forward transfer?

Arslan Chaudhry, Jiefeng Chen, Timothy Nguyen, Dilan Gorur

ICLR

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Denoising diffusion samplers

Francisco Vargas, Will Grathwohl, Arnaud Doucet

ICLR

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Graph schemas as abstractions for transfer learning, inference, and planning

Swaroop Guntupalli, Rajkumar Vasudeva Raju, Shrinu Kushagra, Carter Wendelken, Danny Sawyer, Ishan Deshpande, Stannis Zhou, Miguel Lazaro-Gredilla, Dileep George

arXiv

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Universal Agent Mixtures and the Geometry of Intelligence

Samuel Allen Alexander *, David Quarel *, Len Du *, Marcus Hutter

AISTATS

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Equivariant MuZero

Andreea Deac *, Theophane Weber, George Papamakarios

arXiv

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3D Neural Embedding Likelihood for Robust Sim-to-Real Transfer in Inverse Graphics

Stannis Zhou, Nishad Gothoskar *, Lirui Wang *, Josh Tenenbaum *, Dan Gutfreund *, Miguel Lazaro-Gredilla, Dileep George, Vikash Mansinghka *

arXiv

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Exploration via Epistemic Value Estimation

Simon Schmitt, John Shawe-Taylor *, Hado van Hasselt

AAAI

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Learning Noisy OR Bayesian Networks with Max-Product Belief Propagation

Antoine Dedieu, Stannis Zhou, Dileep George, Miguel Lazaro-Gredilla

arXiv

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Dual Algorithmic Reasoning

Danilo Numeroso *, Davide Bacciu *, Petar Veličković

ICLR

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Distilling Internet-Scale Vision-Language Models into Embodied Agents

Kenneth Marino, Arun Ahuja, Rob Fergus, Ishita Dasgupta, Theodore Sumers *

arXiv

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Pragmatic Fairness: Developing Policies with Outcome Disparity Control

Silvia Chiappa, Alan Malek, Ricardo Silva *, Siyuan Guo *

arXiv

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On a continuous time model of gradient descent dynamics and instability in deep learning

Mihaela Rosca, Yan Wu, Chongli Qin, Benoit Dherin *

Transactions on Machine Learning Research (TMLR)

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Discovering Quantum Phase Transitions with Fermionic Neural Networks

Gino Cassella *, Halvard Sutterud *, Matthew Foulkes *, David Pfau, Neil Drummond *, Sam Azadi *, James Spencer

Physical Review Letters

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Transformer Grammars: Augmenting Transformer Language Models with a Syntactic Inductive Bias

Laurent Sartran, Samuel Barrett *, Adhi Kuncoro, Miloš Stanojević, Phil Blunsom *, Chris Dyer

Transactions of the Association for Computational Linguistics (TACL)

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Confident Approximate Policy Iteration for Efficient Local Planning in q^π-realizable MDPs

Andras Gyorgy, Csaba Szepesvari, Tadashi Kozuno *, Gellért Weisz

NeurIPS

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Particle-Based Score Estimation for Jointly Learning Transition and Observation Models in Autonomous Driving

Angad Singh *, Omar Makhlouf *, Maximilian Igl *, Joao Messias *, Arnaud Doucet, Shimon Whiteson *

CoRL

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ipie: A Python-based Auxiliary-Field Quantum Monte Carlo Program with Flexibility and Efficiency on CPUs and GPUs

Fionn Malone *, Ankit Mahajan *, James Spencer, Joonho Lee *

Journal of Chemical Theory and Computation

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Score-based generative models learn manifold-like structures with constrained mixing

Kevin Li, Ben Moran

NeurIPS Workshop

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Reasoning-Modulated Representations

Petar Veličković, Matko Bošnjak, Thomas Kipf *, Alexander Lerchner, Raia Hadsell, Razvan Pascanu, Charles Blundell

LoG

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Learning Graph Search Heuristics

Michal Pándy *, Weikang Qiu *, Gabriele Corso *, Petar Veličković, Rex Ying *, Jure Leskovec *, Pietro Liò *

LoG

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Continuous Neural Algorithmic Planners

Yu He *, Petar Veličković, Pietro Liò *, Andreea-Ioana Deac

LoG

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Unbiased and Efficient Sampling of Dependency Trees

Miloš Stanojević

EMNLP

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A Generalist Neural Algorithmic Learner

Borja Ibarz, Vitaly Kurin, George Papamakarios, Kyriacos Nikiforou, Mehdi Bennani, Róbert Csordás, Andrew Dudzik, Matko Bošnjak, Alex Vitvitskyi, Yulia Rubanova, Andreea-Ioana Deac, Beatrice Bevilacqua, Yaroslav Ganin, Charles Blundell, Petar Veličković

LoG

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BLaDE: Robust Exploration via Diffusion Models

Daniel Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Shantanu Thakoor

Deep RL Workshop NeurIPS

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