I am a Senior Research Scientist at Google DeepMind. My current research interests are in building compositional generative models, and the associated inference and learning problems.
Before Google DeepMind, I worked for 3 years as a Research Scientist at Vicarious AI (acquired by Alphabet). I obtained my Ph.D. in applied math at Brown University, advised by Prof. Stuart Geman, and my bachelor degrees from Peking University.
Most recent publications on Google Scholar.
‡ indicates equal contribution.
PGMax: Factor Graphs for Discrete Probabilistic Graphical Models and Loopy Belief Propagation in JAX
Guangyao Zhou, Antoine Dedieu, Nishanth Kumar, Wolfgang Lehrach, Miguel Lázaro-Gredilla, Shrinu Kushagra, Dileep George
Accepted conditioned on minor revisions, Journal of Machine Learning Research MLOSS, 2024+
RoboTAP: Tracking Arbitrary Points for Few-Shot Visual Imitation
Mel Vecerik, Carl Doersch, Yi Yang, Todor Davchev, Yusuf Aytar, Guangyao Zhou, Raia Hadsell, Lourdes Agapito, Jon Scholz
International Conference on Robotics and Automation (ICRA), 2024
Learning Cognitive Maps from Transformer Representations for Efficient Planning in Partially Observed Environments
Antoine Dedieu, Wolfgang Lehrach, Guangyao Zhou, Dileep George, Miguel Lázaro-Gredilla
International Conference on Machine Learning (ICML), 2024
Space is a latent sequence: Structured sequence learning as a unified theory of representation in the hippocampus
Rajkumar Vasudeva Raju, J Swaroop Guntupalli, Guangyao Zhou, Miguel Lázaro-Gredilla, Dileep George
Accepted, Science Advances, 2024+
3D Neural Embedding Likelihood: Probabilistic Inverse Graphics for Robust 6D Pose Estimation
Guangyao Zhou‡, Nishad Gothoskar‡, Lirui Wang, Joshua B Tenenbaum, Dan Gutfreund, Miguel Lázaro-Gredilla, Dileep George, Vikash K Mansinghka
International Conference on Computer Vision (ICCV), 2023
Graph schemas as abstractions for transfer learning, inference, and planning
J Swaroop Guntupalli, Rajkumar Vasudeva Raju, Shrinu Kushagra, Carter Wendelken, Danny Sawyer, Ishan Deshpande, Guangyao Zhou, Miguel Lázaro-Gredilla, Dileep George
arXiv:2302.07350, 2023
Learning noisy-OR Bayesian Networks with Max-Product Belief Propagation
Antoine Dedieu, Guangyao Zhou, Dileep George, Miguel Lázaro-Gredilla
International Conference on Machine Learning (ICML), 2023
Metropolis Augmented Hamiltonian Monte Carlo
Guangyao Zhou
Symposium on Advances in Approximate Bayesian Inference (AABI) 2022
Graphical Models with Attention for Context-Specific Independence and an Application to Perceptual Grouping
Guangyao Zhou, Wolfgang Lehrach, Antoine Dedieu, Miguel Lázaro-Gredilla, Dileep George
arXiv:2112.03371, 2021
Query Training: Learning a Worse Model to Infer Better Marginals in Undirected Graphical Models with Hidden Variables
Miguel Lázaro-Gredilla, Wolfgang Lehrach, Nishad Gothoskar, Guangyao Zhou,
Antoine Dedieu, Dileep George
AAAI Conference on Artificial Ingelligence (AAAI) 2021
Mixed Hamiltonian Monte Carlo for mixed discrete and continuous variables
Guangyao Zhou
Advances in Neural Information Processing Systems (NeurIPS) 2020
Extended abstract accepted as talk at PROBPROG 2020
A detailed mathematical theory of thalamic and cortical microcircuits based on inference
in a generative vision model
Dileep George, Miguel Lázaro-Gredilla, Wolfgang Lehrach, Antoine Dedieu, Guangyao Zhou
bioRxiv 2020.09.09.290601, 2020
Capacities and efficient computation of first passage probabilities
Jackson Loper‡, Guangyao Zhou‡, Stuart Geman
Phys. Rev. E 102, 023304, 2020
Base-pair ambiguity and the kinetics of RNA folding
Guangyao Zhou, Jackson Loper, Stuart Geman
BMC Bioinformatics, 20(1):666, 2019
Sparse feature selection by information theory
Guangyao Zhou, Stuart Geman, Joachim M Buhmann
IEEE International Symposium on Information Theory (ISIT), 2014
L1-graph construction using structured sparsity
Guangyao Zhou, Zhiwu Lu, Yuxin Peng
Neurocomputing, 120:441-452, 2013
Full Resume in PDF.
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