Daniel Hernández-Lobato

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José Miguel Hernández-LobatoUniversity Lecturer (US Assistant Professor) 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
Thang D BuiLecturer, University of SydneyVerified email at sydney.edu.au
Yingzhen LiImperial College LondonVerified email at imperial.ac.uk
Eduardo C. Garrido-MerchánTeaching Assistant at Universidad Autonoma de MadridVerified email at uam.es
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
Novi QuadriantoReader in Machine Learning, Predictive Analytics Lab (PAL), University of SussexVerified email at sussex.ac.uk
Carlos Villacampa-CalvoUniversidad Autónoma de MadridVerified email at uam.es
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
Víctor SotoColumbia UniversityVerified email at cs.columbia.edu
Laura Cornejo-BuenoAssistant Professor, Rey Juan Carlos UniversityVerified email at urjc.es
Sancho Salcedo-SanzCatedrático de Universidad (Full Professor), Universidad de AlcaláVerified email at uah.es
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 Martinez-Munoz, D Hernández-Lobato, A Suárez IEEE Transactions on Pattern Analysis and Machine Intelligence 31 (2), 245-259, 2008	329	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	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
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
Dealing with categorical and integer-valued variables in bayesian optimization with gaussian processes EC Garrido-Merchán, D Hernández-Lobato Neurocomputing 380, 20-35, 2020	72	2020
How large should ensembles of classifiers be? D Hernández-Lobato, G Martínez-Muñoz, A Suárez Pattern Recognition 46 (5), 1323-1336, 2013	70	2013
Robust multi-class Gaussian process classification D Hernández-Lobato, J Hernández-lobato, P Dupont Advances in neural information processing systems 24, 280-288, 2011	68	2011
Statistical instance-based pruning in ensembles of independent classifiers D Hernández-Lobato, G Martinez-Munoz, A Suárez IEEE Transactions on Pattern Analysis and Machine Intelligence 31 (2), 364-369, 2008	61	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	55	2015
Scalable Gaussian process classification via expectation propagation D Hernández-Lobato, JM Hernández-Lobato Artificial Intelligence and Statistics, 168-176, 2016	52	2016
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	48	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	40	2006
Mind the nuisance: Gaussian process classification using privileged noise D Hernández-Lobato, V Sharmanska, K Kersting, CH Lampert, ... arXiv preprint arXiv:1407.0179, 2014	39	2014
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	36	2018
Expectation propagation for microarray data classification D Hernández-Lobato, JM Hernández-Lobato, A Suárez Pattern recognition letters 31 (12), 1618-1626, 2010	36	2010
Class-switching neural network ensembles G Martínez-Muñoz, A Sánchez-Martínez, D Hernández-Lobato, A Suárez Neurocomputing 71 (13-15), 2521-2528, 2008	32	2008
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
Predictive entropy search for multi-objective bayesian optimization with constraints EC Garrido-Merchán, D Hernández-Lobato Neurocomputing 361, 50-68, 2019	28	2019
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	26	2015

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