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
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
Cheng ZhangMicrosoft Research, CambridgeVerified email at kth.se
Brooks PaigeAssociate Professor, University College LondonVerified email at ucl.ac.uk
Neil HoulsbyGoogleVerified email at google.com
David BrooksHaley Family Professor of Computer Science, Harvard UniversityVerified email at eecs.harvard.edu
Gu-Yeon WeiProfessor of EE and CS, Harvard UniversityVerified email at eecs.harvard.edu
Alberto SuárezProfessor of Artificial Intelligence, Computer Science Department, Universidad Autónoma de MadridVerified email at uam.es
Thang D BuiLecturer, University of SydneyVerified email at sydney.edu.au
Jorge Aguilera-IparraguirreRecursion PharmaceuticalsVerified email at recursionpharma.com
David DuvenaudAssistant Professor, University of TorontoVerified email at cs.toronto.edu
Sebastian NowozinPartner Research Manager, Machine Intelligence, Microsoft Research (Cambridge, UK)Verified email at microsoft.com
Matthew W. HoffmanDeepMindVerified email at google.com
Stefan DepewegSiemens AGVerified email at uos.de
Steffen UdluftLearning Systems, SiemensVerified email at siemens.com

José Miguel Hernández-Lobato

University Lecturer (US Assistant Professor) 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	1189	2018
Probabilistic backpropagation for scalable learning of bayesian neural networks JM Hernández-Lobato, R Adams International Conference on Machine Learning, 1861-1869, 2015	575	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	448	2016
Predictive entropy search for efficient global optimization of black-box functions JM Hernández-Lobato, MW Hoffman, Z Ghahramani arXiv preprint arXiv:1406.2541, 2014	440	2014
Grammar variational autoencoder MJ Kusner, B Paige, JM Hernández-Lobato International Conference on Machine Learning, 1945-1954, 2017	403	2017
Gans for sequences of discrete elements with the gumbel-softmax distribution MJ Kusner, JM Hernández-Lobato arXiv preprint arXiv:1611.04051, 2016	212	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	160	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	153	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	117	2016
Collaborative Gaussian processes for preference learning N Houlsby, F Huszar, Z Ghahramani, J Hernández-lobato Advances in neural information processing systems 25, 2096-2104, 2012	117	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	110	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	97	2018
Generalized Spike-and-Slab Priors for Bayesian Group Feature Selection Using Expectation Propagation. D Hernández-Lobato, JM Hernández-Lobato, P Dupont Journal of Machine Learning Research 14 (7), 2013	95	2013
Stochastic expectation propagation Y Li, JM Hernández-Lobato, RE Turner arXiv preprint arXiv:1506.04132, 2015	94	2015
Probabilistic matrix factorization with non-random missing data JM Hernández-Lobato, N Houlsby, Z Ghahramani International Conference on Machine Learning, 1512-1520, 2014	93	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	92	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	89	2017
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	87	2017
A general framework for constrained Bayesian optimization using information-based search JM Hernández-Lobato, MA Gelbart, RP Adams, MW Hoffman, ... MIT Press, 2016	84	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	77	2018

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