José Miguel Hernández-Lobato - Google Scholar Citations

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	All	Since 2015
Citations	4332	4140
h-index	29	27
i10-index	61	55

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Daniel Hernández-LobatoLecturer of Computer Science, Unviersidad Autónoma de MadridVerified email at uam.es
Zoubin GhahramaniProfessor, University of Cambridge, and Chief Scientist, UberVerified email at eng.cam.ac.uk
Ryan P. AdamsPrinceton UniversityVerified email at princeton.edu
Richard E TurnerUniversity Reader, University of CambridgeVerified email at cam.ac.uk
Yingzhen LiMicrosoft Research CambridgeVerified email at cam.ac.uk
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
Neil HoulsbyGoogleVerified email at google.com
Thang D BuiResearch Scientist, Uber AI; Lecturer, University of SydneyVerified email at sydney.edu.au
Alberto SuárezProfessor of Artificial Intelligence, Computer Science Department, Universidad Autónoma de MadridVerified email at uam.es
Brooks PaigeAssociate Professor, University College LondonVerified email at ucl.ac.uk
Cheng ZhangMicrosoft Research, CambridgeVerified email at kth.se
Matthew W. HoffmanUniversity of CambridgeVerified email at cam.ac.uk
Sebastian NowozinMicrosoft Research (Cambridge, UK)Verified email at microsoft.com
Jorge Aguilera-IparraguirreRecursion PharmaceuticalsVerified email at recursionpharma.com
David DuvenaudAssistant Professor, University of TorontoVerified email at cs.toronto.edu
Stefan DepewegSiemens AGVerified email at uos.de
Michael (Mike) GelbartUniversity of British ColumbiaVerified email at cs.ubc.ca
Brandon ReagenResearch Scientist, FacebookVerified email at fb.com
David BrooksHaley Family Professor of Computer Science, Harvard UniversityVerified email at eecs.harvard.edu

José Miguel Hernández-Lobato

University Lecturer (US Assistant Professor) University of Cambridge

Verified email at cam.ac.uk - Homepage

Bayesian Optimization Bayesian Neural Networks Conditional Copulas Bayesian Sparsity Approximate Inference


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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	771	2018
Probabilistic backpropagation for scalable learning of bayesian neural networks JM Hernández-Lobato, R Adams International Conference on Machine Learning, 1861-1869, 2015	434	2015
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	336	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, 918-926, 2014	327	2014
Grammar variational autoencoder MJ Kusner, B Paige, JM Hernández-Lobato arXiv preprint arXiv:1703.01925, 2017	246	2017
Gans for sequences of discrete elements with the gumbel-softmax distribution MJ Kusner, JM Hernández-Lobato arXiv preprint arXiv:1611.04051, 2016	155	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	125	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	118	2016
Collaborative Gaussian processes for preference learning N Houlsby, F Huszar, Z Ghahramani, JM Hernández-Lobato Advances in neural information processing systems, 2096-2104, 2012	99	2012
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	93	2016
Generalized spike-and-slab priors for Bayesian group feature selection using expectation propagation D Hernández-Lobato, JM Hernández-Lobato, P Dupont The Journal of Machine Learning Research 14 (1), 1891-1945, 2013	91	2013
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	77	2016
Stochastic expectation propagation Y Li, JM Hernández-Lobato, RE Turner Advances in neural information processing systems, 2323-2331, 2015	77	2015
Probabilistic matrix factorization with non-random missing data JM Hernández-Lobato, N Houlsby, Z Ghahramani International Conference on Machine Learning, 1512-1520, 2014	72	2014
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	71	2015
Parallel and distributed Thompson sampling for large-scale accelerated exploration of chemical space JM Hernández-Lobato, J Requeima, EO Pyzer-Knapp, A Aspuru-Guzik arXiv preprint arXiv:1706.01825, 2017	67	2017
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	63	2017
A general framework for constrained Bayesian optimization using information-based search JM Hernández-Lobato, MA Gelbart, RP Adams, MW Hoffman, ... The Journal of Machine Learning Research 17 (1), 5549-5601, 2016	57	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	51	2018
Robust multi-class Gaussian process classification D Hernández-Lobato, JM Hernández-Lobato, P Dupont Advances in neural information processing systems, 280-288, 2011	51	2011

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