Daniel Hernández-Lobato

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José Miguel Hernández-LobatoAssociate Professor in Machine Learning, University of CambridgeVerified email at cam.ac.uk
Alberto SuárezProfessor of Artificial Intelligence, Computer Science Department, Universidad Autónoma de MadridVerified email at uam.es
Gonzalo Martínez MuñozUniversidad Autónoma de MadridVerified email at uam.es
Richard E TurnerProfessor, University of CambridgeVerified email at cam.ac.uk
Eduardo C. Garrido-MerchánMachine Learning Assistant Professor at Universidad Pontificia de ComillasVerified email at icade.comillas.edu
Thang D BuiLecturer, University of SydneyVerified email at sydney.edu.au
Yingzhen LiImperial College LondonVerified email at imperial.ac.uk
Pierre DupontProfessor of Computer Science, ICTEAM, EPL, Université catholique de LouvainVerified email at uclouvain.be
Ryan P. AdamsPrinceton UniversityVerified email at princeton.edu
Amar ShahPhD student, University of CambridgeVerified email at cam.ac.uk
Carlos Villacampa-CalvoUniversidad Autónoma de MadridVerified email at uam.es
Novi QuadriantoReader in Machine Learning, Predictive Analytics Lab (WeArePAL), University of SussexVerified email at sussex.ac.uk
Mark RowlandResearch Scientist, DeepMindVerified email at google.com
Viktoriia SharmanskaImperial College Research FellowVerified email at imperial.ac.uk
Lauwerys BUCB BiopharmaVerified email at ucb.com
Laura Cornejo-BuenoAssistant Professor, University of AlcaláVerified email at uah.es
Sancho Salcedo-SanzCatedrático de Universidad (Full Professor), Universidad de AlcaláVerified email at uah.es
Víctor SotoAmazon Alexa AIVerified email at cs.columbia.edu
Houssiau FAProfesseur; IREC, SSS, Université catholique de LouvainVerified email at uclouvain.be
Julie DucreuxChargé de Recherche, Université catholique de LouvainVerified email at uclouvain.be

Daniel Hernández-Lobato

Lecturer of Computer Science, Universidad Autónoma de Madrid

Verified email at uam.es - Homepage

Machine Learning - Bayesian Models - Feature Selection - Kernel Methods - Approximate Inference - Gaussian Processes - Ensemble


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An analysis of ensemble pruning techniques based on ordered aggregation G Martínez-Muñoz, D Hernández-Lobato, A Suárez IEEE Transactions on Pattern Analysis and Machine Intelligence 31 (2), 245-259, 2008	355	2008
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	209	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	190	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	163	2016
Dealing with categorical and integer-valued variables in bayesian optimization with gaussian processes EC Garrido-Merchán, D Hernández-Lobato Neurocomputing 380, 20-35, 2020	124	2020
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, 2013	101	2013
How large should ensembles of classifiers be? D Hernández-Lobato, G Martínez-Muñoz, A Suárez Pattern Recognition 46 (5), 1323-1336, 2013	81	2013
Robust multi-class Gaussian process classification D Hernández-Lobato, J Hernández-lobato, P Dupont Advances in neural information processing systems 24, 2011	74	2011
Statistical instance-based pruning in ensembles of independent classifiers D Hernández-Lobato, G Martínez-Muñoz, A Suárez IEEE Transactions on Pattern Analysis and Machine Intelligence 31 (2), 364-369, 2008	65	2008
Expectation propagation in linear regression models with spike-and-slab priors JM Hernández-Lobato, D Hernández-Lobato, A Suárez Machine Learning 99 (3), 437-487, 2015	61	2015
Scalable Gaussian process classification via expectation propagation D Hernández-Lobato, JM Hernández-Lobato Artificial Intelligence and Statistics, 168-176, 2016	55	2016
Predictive entropy search for multi-objective bayesian optimization with constraints EC Garrido-Merchán, D Hernández-Lobato Neurocomputing 361, 50-68, 2019	52	2019
Bayesian optimization of a hybrid system for robust ocean wave features prediction L Cornejo-Bueno, EC Garrido-Merchán, D Hernández-Lobato, ... Neurocomputing 275, 818-828, 2018	52	2018
Empirical analysis and evaluation of approximate techniques for pruning regression bagging ensembles D Hernández-Lobato, G Martínez-Muñoz, A Suárez Neurocomputing 74 (12-13), 2250-2264, 2011	49	2011
Pruning in ordered regression bagging ensembles D Hernández-Lobato, G Martínez-Muñoz, A Suárez The 2006 IEEE International Joint Conference on Neural Network Proceedings …, 2006	42	2006
Mind the nuisance: Gaussian process classification using privileged noise D Hernández-Lobato, V Sharmanska, K Kersting, CH Lampert, ... Advances in Neural Information Processing Systems 27, 2014	41	2014
Expectation propagation for microarray data classification D Hernández-Lobato, JM Hernández-Lobato, A Suárez Pattern recognition letters 31 (12), 1618-1626, 2010	37	2010
Class-switching neural network ensembles G Martínez-Muñoz, A Sánchez-Martínez, D Hernández-Lobato, A Suárez Neurocomputing 71 (13-15), 2521-2528, 2008	35	2008
Heterogeneity of synovial molecular patterns in patients with arthritis BR Lauwerys, D Hernández-Lobato, P Gramme, J Ducreux, A Dessy, ... PloS one 10 (4), e0122104, 2015	33	2015
Expectation propagation for Bayesian multi-task feature selection D Hernández-Lobato, JM Hernández-Lobato, T Helleputte, P Dupont Joint European Conference on Machine Learning and Knowledge Discovery in …, 2010	29	2010

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