José Miguel Hernández-Lobato

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Daniel Hernández-LobatoAssociate Professor, Universidad Autónoma de MadridVerified email at uam.es
Richard E TurnerProfessor, University of CambridgeVerified email at cam.ac.uk
Ryan P. AdamsPrinceton UniversityVerified email at princeton.edu
Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, GoogleVerified email at eng.cam.ac.uk
Javier AntoranUniversity of CambridgeVerified email at cam.ac.uk
Cheng ZhangGenAI, MetaVerified email at meta.com
Yingzhen LiImperial College LondonVerified email at imperial.ac.uk
Alan Aspuru-GuzikProfessor of Chemistry and Computer Science, University of Toronto (starting July 1, 2018)Verified email at utoronto.ca
Matt J. KusnerUniversity College LondonVerified email at ucl.ac.uk
Brooks PaigeAssociate Professor, University College LondonVerified email at ucl.ac.uk
Sebastian TschiatschekUniversity of ViennaVerified email at univie.ac.at
Eric NalisnickAssistant Professor, Johns Hopkins UniversityVerified email at jhu.edu
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHVerified email at tuebingen.mpg.de
Chao MaSenior Researcher, Microsoft Research CambridgeVerified email at microsoft.com
Austin TrippUniversity of CambridgeVerified email at cam.ac.uk
Neil HoulsbyGoogleVerified email at google.com
James Urquhart AllinghamGoogle DeepMindVerified email at cam.ac.uk
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 UniversityVerified email at eecs.harvard.edu
Jorge Aguilera-IparraguirreRecursion PharmaceuticalsVerified email at recursionpharma.com

José Miguel Hernández-Lobato

Professor of 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	3411	2018
Probabilistic backpropagation for scalable learning of bayesian neural networks JM Hernández-Lobato, R Adams International conference on machine learning, 1861-1869, 2015	1146	2015
Grammar variational autoencoder MJ Kusner, B Paige, JM Hernández-Lobato International conference on machine learning, 1945-1954, 2017	1090	2017
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	807	2014
Minerva: Enabling low-power, highly-accurate deep neural network accelerators B Reagen, P Whatmough, R Adolf, S Rama, H Lee, SK Lee, ... ACM SIGARCH Computer Architecture News 44 (3), 267-278, 2016	749	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	446	2018
Constrained Bayesian Optimization for Automatic Chemical Design RR Griffiths, JM Hernández-Lobato arXiv preprint arXiv:1709.05501, 2017	421*	2017
Gans for sequences of discrete elements with the gumbel-softmax distribution MJ Kusner, JM Hernández-Lobato arXiv preprint arXiv:1611.04051, 2016	382	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	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	271	2016
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	263	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	220	2018
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	202	2017
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	199	2016
Probabilistic matrix factorization with non-random missing data JM Hernández-Lobato, N Houlsby, Z Ghahramani International conference on machine learning, 1512-1520, 2014	199	2014
A general framework for constrained Bayesian optimization using information-based search JM Hern, MA Gelbart, RP Adams, MW Hoffman, Z Ghahramani Journal of Machine Learning Research 17 (160), 1-53, 2016	192	2016
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	185	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	183	2015
Collaborative gaussian processes for preference learning N Houlsby, F Huszar, Z Ghahramani, J Hernández-lobato Advances in neural information processing systems 25, 2012	160	2012
Stochastic expectation propagation Y Li, JM Hernández-Lobato, RE Turner Advances in neural information processing systems 28, 2015	157	2015

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