José Miguel Hernández-Lobato

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Daniel Hernández-LobatoLecturer of Computer Science, Universidad Autónoma de MadridVerified email at uam.es
Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, GoogleVerified email at eng.cam.ac.uk
Ryan P. AdamsPrinceton UniversityVerified email at princeton.edu
Richard E TurnerProfessor, University of CambridgeVerified email at cam.ac.uk
Yingzhen LiImperial College LondonVerified email at imperial.ac.uk
Cheng ZhangPrincipal Researcher, Microsoft Research, Cambridge, UKVerified email at kth.se
Alan Aspuru-GuzikProfessor of Chemistry and Computer Science, University of Toronto (starting July 1, 2018)Verified email at utoronto.ca
Matt J. KusnerAssociate Professor, University College LondonVerified email at ucl.ac.uk
Brooks PaigeAssociate Professor, University College LondonVerified email at ucl.ac.uk
Chao MaMachine Learning Group, Computational and Biological Learning Lab, University of CambridgeVerified email at cam.ac.uk
Neil HoulsbyGoogleVerified email at google.com
David BrooksHaley Family Professor of Computer Science, Harvard UniversityVerified email at eecs.harvard.edu
Gu-Yeon WeiRobert and Suzanne Case Professor of EE and CS, Harvard University and Fellow, Samsung ResearchVerified email at eecs.harvard.edu
Javier AntoranUniversity of CambridgeVerified email at cam.ac.uk
Thang D BuiLecturer, University of SydneyVerified email at sydney.edu.au
Alberto SuárezProfessor of Artificial Intelligence, Computer Science Department, Universidad Autónoma de MadridVerified email at uam.es
Jorge Aguilera-IparraguirreRecursion PharmaceuticalsVerified email at recursionpharma.com
David DuvenaudAssistant Professor, University of TorontoVerified email at cs.toronto.edu
Marton HavasiUniversity of CambridgeVerified email at cam.ac.uk
Stefan DepewegSiemens AGVerified email at uos.de

José Miguel Hernández-Lobato

Associate Professor in Machine Learning, University of Cambridge

Verified email at cam.ac.uk - Homepage

Bayesian deep learning approximate inference deep generative modeling automatic molecular design reinforcement learning


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Automatic chemical design using a data-driven continuous representation of molecules R Gómez-Bombarelli, JN Wei, D Duvenaud, JM Hernández-Lobato, ... ACS central science 4 (2), 268-276, 2018	1861	2018
Probabilistic backpropagation for scalable learning of bayesian neural networks JM Hernández-Lobato, R Adams International conference on machine learning, 1861-1869, 2015	789	2015
Grammar variational autoencoder MJ Kusner, B Paige, JM Hernández-Lobato International conference on machine learning, 1945-1954, 2017	661	2017
Minerva: Enabling low-power, highly-accurate deep neural network accelerators B Reagen, P Whatmough, R Adolf, S Rama, H Lee, SK Lee, ... 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture …, 2016	584	2016
Predictive entropy search for efficient global optimization of black-box functions JM Hernández-Lobato, MW Hoffman, Z Ghahramani Advances in neural information processing systems 27, 2014	546	2014
Gans for sequences of discrete elements with the gumbel-softmax distribution MJ Kusner, JM Hernández-Lobato arXiv preprint arXiv:1611.04051, 2016	272	2016
Deep Gaussian processes for regression using approximate expectation propagation T Bui, D Hernández-Lobato, J Hernandez-Lobato, Y Li, R Turner International conference on machine learning, 1472-1481, 2016	217	2016
Black-box alpha divergence minimization J Hernandez-Lobato, Y Li, M Rowland, T Bui, D Hernández-Lobato, ... International Conference on Machine Learning, 1511-1520, 2016	198	2016
Decomposition of uncertainty in Bayesian deep learning for efficient and risk-sensitive learning S Depeweg, JM Hernandez-Lobato, F Doshi-Velez, S Udluft International Conference on Machine Learning, 1184-1193, 2018	183	2018
Predictive entropy search for multi-objective bayesian optimization D Hernández-Lobato, J Hernandez-Lobato, A Shah, R Adams International conference on machine learning, 1492-1501, 2016	170	2016
Learning and policy search in stochastic dynamical systems with bayesian neural networks S Depeweg, JM Hernández-Lobato, F Doshi-Velez, S Udluft arXiv preprint arXiv:1605.07127, 2016	144	2016
Deterministic variational inference for robust bayesian neural networks A Wu, S Nowozin, E Meeds, RE Turner, JM Hernandez-Lobato, AL Gaunt arXiv preprint arXiv:1810.03958, 2018	134	2018
Probabilistic matrix factorization with non-random missing data JM Hernández-Lobato, N Houlsby, Z Ghahramani International Conference on Machine Learning, 1512-1520, 2014	132	2014
Collaborative gaussian processes for preference learning N Houlsby, F Huszar, Z Ghahramani, J Hernández-lobato Advances in neural information processing systems 25, 2012	128	2012
Constrained Bayesian optimization for automatic chemical design using variational autoencoders RR Griffiths, JM Hernández-Lobato Chemical science 11 (2), 577-586, 2020	126	2020
Parallel and distributed Thompson sampling for large-scale accelerated exploration of chemical space JM Hernández-Lobato, J Requeima, EO Pyzer-Knapp, A Aspuru-Guzik International conference on machine learning, 1470-1479, 2017	124	2017
Predictive entropy search for Bayesian optimization with unknown constraints JM Hernández-Lobato, M Gelbart, M Hoffman, R Adams, Z Ghahramani International conference on machine learning, 1699-1707, 2015	124	2015
Stochastic expectation propagation Y Li, JM Hernández-Lobato, RE Turner Advances in neural information processing systems 28, 2015	121	2015
Sequence tutor: Conservative fine-tuning of sequence generation models with kl-control N Jaques, S Gu, D Bahdanau, JM Hernández-Lobato, RE Turner, D Eck International Conference on Machine Learning, 1645-1654, 2017	115	2017
A general framework for constrained Bayesian optimization using information-based search JM Hernández-Lobato, MA Gelbart, RP Adams, MW Hoffman, ... MIT Press, 2016	115	2016

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