‪José Miguel Hernández-Lobato‬

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Daniel Hernández-LobatoAssociate Professor, Universidad Autónoma de MadridAdresse e-mail validée de uam.es
Richard E TurnerProfessor, University of CambridgeAdresse e-mail validée de cam.ac.uk
Ryan P. AdamsPrinceton UniversityAdresse e-mail validée de princeton.edu
Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, GoogleAdresse e-mail validée de eng.cam.ac.uk
Javier AntoranUniversity of CambridgeAdresse e-mail validée de cam.ac.uk
Cheng ZhangGenAI, MetaAdresse e-mail validée de meta.com
Yingzhen LiImperial College LondonAdresse e-mail validée de imperial.ac.uk
Alan Aspuru-GuzikProfessor of Chemistry and Computer Science, University of Toronto (starting July 1, 2018)Adresse e-mail validée de utoronto.ca
Brooks PaigeAssociate Professor, University College LondonAdresse e-mail validée de ucl.ac.uk
Matt J. KusnerUniversity College LondonAdresse e-mail validée de ucl.ac.uk
Sebastian TschiatschekUniversity of ViennaAdresse e-mail validée de univie.ac.at
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHAdresse e-mail validée de tuebingen.mpg.de
Eric NalisnickAssistant Professor, Johns Hopkins UniversityAdresse e-mail validée de jhu.edu
Austin TrippValence LabsAdresse e-mail validée de valencelabs.com
Chao MaSenior Researcher, Microsoft Research CambridgeAdresse e-mail validée de microsoft.com
Neil HoulsbyGoogleAdresse e-mail validée de google.com
Thang D BuiLecturer, Australian National UniversityAdresse e-mail validée de anu.edu.au
James Urquhart AllinghamGoogle DeepMindAdresse e-mail validée de cam.ac.uk
David BrooksHaley Family Professor of Computer Science, Harvard UniversityAdresse e-mail validée de eecs.harvard.edu
Gu-Yeon WeiRobert and Suzanne Case Professor of EE and CS, Harvard UniversityAdresse e-mail validée de eecs.harvard.edu

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José Miguel Hernández-Lobato

Professor of Machine Learning, University of Cambridge

Adresse e-mail validée de cam.ac.uk - Page d'accueil

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


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
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	3695	2018
Probabilistic backpropagation for scalable learning of bayesian neural networks JM Hernández-Lobato, R Adams International conference on machine learning, 1861-1869, 2015	1208	2015
Grammar variational autoencoder MJ Kusner, B Paige, JM Hernández-Lobato International conference on machine learning, 1945-1954, 2017	1170	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	859	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	776	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	495	2018
Constrained Bayesian Optimization for Automatic Chemical Design RR Griffiths, JM Hernández-Lobato arXiv preprint arXiv:1709.05501, 2017	465*	2017
Gans for sequences of discrete elements with the gumbel-softmax distribution MJ Kusner, JM Hernández-Lobato arXiv preprint arXiv:1611.04051, 2016	388	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	283	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	280	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	274	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	231	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	222	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	212	2016
Probabilistic matrix factorization with non-random missing data JM Hernández-Lobato, N Houlsby, Z Ghahramani International conference on machine learning, 1512-1520, 2014	206	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	204	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	198	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	195	2017
Sample-efficient optimization in the latent space of deep generative models via weighted retraining A Tripp, E Daxberger, JM Hernández-Lobato Advances in Neural Information Processing Systems 33, 11259-11272, 2020	163	2020
Collaborative gaussian processes for preference learning N Houlsby, F Huszar, Z Ghahramani, J Hernández-lobato Advances in neural information processing systems 25, 2012	162	2012

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