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 of the Quantitative Methods Department, ICADE. Universidad Pontificia ComillasVerified 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
Simón Rodríguez SantanaEscuela Técnica Superior de Ingeniería (ICAI) - Universidad de ComillasVerified email at comillas.edu
Amar ShahPhD student, University of CambridgeVerified email at cam.ac.uk
Bryan ZaldivarCSIC - IFIC (Valencia)Verified email at csic.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
Laura Cornejo-BuenoAssistant Professor, University of AlcaláVerified email at uah.es
Sancho Salcedo-SanzCatedrático de Teoría de la Señal, Universidad de AlcaláVerified email at uah.es
Lauwerys BUCB BiopharmaVerified email at ucb.com
Daniel Fernández-SánchezUniversidad Autónoma de MadridVerified email at uam.es

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	394	2008
Dealing with categorical and integer-valued variables in bayesian optimization with gaussian processes EC Garrido-Merchán, D Hernández-Lobato Neurocomputing 380, 20-35, 2020	284	2020
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	272	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	271	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	264	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	114	2013
Robust multi-class Gaussian process classification D Hernández-Lobato, J Hernández-Lobato, P Dupont Advances in neural information processing systems 24, 2011	99	2011
How large should ensembles of classifiers be? D Hernández-Lobato, G Martínez-Muñoz, A Suárez Pattern Recognition 46 (5), 1323-1336, 2013	96	2013
Predictive entropy search for multi-objective bayesian optimization with constraints EC Garrido-Merchán, D Hernández-Lobato Neurocomputing 361, 50-68, 2019	92	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	84	2015
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	80	2018
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	74	2008
Scalable Gaussian process classification via expectation propagation D Hernández-Lobato, JM Hernández-Lobato Artificial Intelligence and Statistics, 168-176, 2016	65	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	55	2011
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	46	2014
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	45	2006
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	42	2016
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	41	2015
Expectation propagation for microarray data classification D Hernández-Lobato, JM Hernández-Lobato, A Suárez Pattern recognition letters 31 (12), 1618-1626, 2010	39	2010
Learning feature selection dependencies in multi-task learning D Hernández-Lobato, JM Hernández-Lobato Advances in Neural Information Processing Systems 26, 2013	34	2013

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