Daniel Hernández-Lobato

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José Miguel Hernández-LobatoProfessor of 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
Eduardo C. Garrido-MerchánAssistant Professor at Faculty of Economics and Business. Universidad Pontificia Comillas, ICADE.Verified email at icade.comillas.edu
Richard E TurnerProfessor, University of CambridgeVerified email at cam.ac.uk
Thang D BuiLecturer, Australian National UniversityVerified email at anu.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
Carlos Villacampa-CalvoVerified email at uam.es
Amar ShahPhD student, University of CambridgeVerified email at cam.ac.uk
Simón Rodríguez SantanaEscuela Técnica Superior de Ingeniería (ICAI) - Universidad de ComillasVerified email at comillas.edu
Bryan ZaldivarCSIC - IFIC (Valencia)Verified email at uam.es
Novi QuadriantoProfessor of Machine Learning, University of Sussex UK, BCAM Spain, Monash IndonesiaVerified email at sussex.ac.uk
Mark RowlandResearch Scientist, Google 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

Daniel Hernández-Lobato

Associate Professor, 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	383	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	258	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	249	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	228	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	218	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 14 (7), 2013	110	2013
Robust multi-class Gaussian process classification D Hernández-Lobato, J Hernández-lobato, P Dupont Advances in neural information processing systems 24, 2011	95	2011
How large should ensembles of classifiers be? D Hernández-Lobato, G Martínez-Muñoz, A Suárez Pattern Recognition 46 (5), 1323-1336, 2013	94	2013
Predictive entropy search for multi-objective bayesian optimization with constraints EC Garrido-Merchán, D Hernández-Lobato Neurocomputing 361, 50-68, 2019	76	2019
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	74	2015
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	73	2008
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	69	2018
Scalable Gaussian process classification via expectation propagation D Hernández-Lobato, JM Hernández-Lobato Artificial Intelligence and Statistics, 168-176, 2016	63	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	53	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	44	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	43	2014
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	38	2015
Expectation propagation for microarray data classification D Hernández-Lobato, JM Hernández-Lobato, A Suárez Pattern recognition letters 31 (12), 1618-1626, 2010	37	2010
Ambiguity helps: Classification with disagreements in crowdsourced annotations V Sharmanska, D Hernández-Lobato, J Miguel Hernandez-Lobato, ... Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016	35	2016
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

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