José Miguel Hernández-Lobato

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Daniel Hernández-LobatoAssociate Professor, Universidad Autónoma de MadridEmail verificata su uam.es
Richard E TurnerProfessor, University of CambridgeEmail verificata su cam.ac.uk
Ryan P. AdamsPrinceton UniversityEmail verificata su princeton.edu
Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, GoogleEmail verificata su eng.cam.ac.uk
Javier AntoranUniversity of CambridgeEmail verificata su cam.ac.uk
Cheng ZhangGenAI, MetaEmail verificata su meta.com
Yingzhen LiImperial College LondonEmail verificata su imperial.ac.uk
Alan Aspuru-GuzikProfessor of Chemistry and Computer Science, University of Toronto (starting July 1, 2018)Email verificata su utoronto.ca
Matt J. KusnerUniversity College LondonEmail verificata su ucl.ac.uk
Brooks PaigeAssociate Professor, University College LondonEmail verificata su ucl.ac.uk
Sebastian TschiatschekUniversity of ViennaEmail verificata su univie.ac.at
Eric NalisnickAssistant Professor, Johns Hopkins UniversityEmail verificata su jhu.edu
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHEmail verificata su tuebingen.mpg.de
Austin TrippUniversity of CambridgeEmail verificata su cam.ac.uk
Chao MaSenior Researcher, Microsoft Research CambridgeEmail verificata su microsoft.com
Neil HoulsbyGoogleEmail verificata su google.com
James Urquhart AllinghamGoogle DeepMindEmail verificata su cam.ac.uk
David BrooksHaley Family Professor of Computer Science, Harvard UniversityEmail verificata su eecs.harvard.edu
Gu-Yeon WeiRobert and Suzanne Case Professor of EE and CS, Harvard UniversityEmail verificata su eecs.harvard.edu
Jorge Aguilera-IparraguirreRecursion PharmaceuticalsEmail verificata su recursionpharma.com

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José Miguel Hernández-Lobato

Professor of Machine Learning, University of Cambridge

Email verificata su cam.ac.uk - Home page

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


Titolo Ordina per citazioni Ordina per anno Ordina per titolo	Citata da Citata da	Anno
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	3554	2018
Probabilistic backpropagation for scalable learning of bayesian neural networks JM Hernández-Lobato, R Adams International conference on machine learning, 1861-1869, 2015	1186	2015
Grammar variational autoencoder MJ Kusner, B Paige, JM Hernández-Lobato International conference on machine learning, 1945-1954, 2017	1137	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	840	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	766	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	470	2018
Constrained Bayesian Optimization for Automatic Chemical Design RR Griffiths, JM Hernández-Lobato arXiv preprint arXiv:1709.05501, 2017	451*	2017
Gans for sequences of discrete elements with the gumbel-softmax distribution MJ Kusner, JM Hernández-Lobato arXiv preprint arXiv:1611.04051, 2016	386	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	278	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	272	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	271	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	226	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	214	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	208	2016
Probabilistic matrix factorization with non-random missing data JM Hernández-Lobato, N Houlsby, Z Ghahramani International conference on machine learning, 1512-1520, 2014	202	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	198	2016
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	192	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	191	2017
Collaborative gaussian processes for preference learning N Houlsby, F Huszar, Z Ghahramani, J Hernández-lobato Advances in neural information processing systems 25, 2012	161	2012
Stochastic expectation propagation Y Li, JM Hernández-Lobato, RE Turner Advances in neural information processing systems 28, 2015	158	2015

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