Daniel Hernández-Lobato

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José Miguel Hernández-LobatoProfessor of Machine Learning, University of CambridgeE-mail confirmado em cam.ac.uk
Alberto SuárezProfessor of Artificial Intelligence, Computer Science Department, Universidad Autónoma de MadridE-mail confirmado em uam.es
Gonzalo Martínez MuñozUniversidad Autónoma de MadridE-mail confirmado em uam.es
Eduardo C. Garrido-MerchánInstitute for Research in Technology - IIT. Universidad Pontificia ComillasE-mail confirmado em icade.comillas.edu
Richard E TurnerProfessor, University of CambridgeE-mail confirmado em cam.ac.uk
Thang D BuiLecturer, Australian National UniversityE-mail confirmado em anu.edu.au
Yingzhen LiImperial College LondonE-mail confirmado em imperial.ac.uk
Pierre DupontProfessor of Computer Science, ICTEAM, EPL, Université catholique de LouvainE-mail confirmado em uclouvain.be
Simón Rodríguez SantanaEscuela Técnica Superior de Ingeniería (ICAI) - Universidad de ComillasE-mail confirmado em comillas.edu
Ryan P. AdamsPrinceton UniversityE-mail confirmado em princeton.edu
Carlos Villacampa-CalvoE-mail confirmado em uam.es
Amar ShahPhD student, University of CambridgeE-mail confirmado em cam.ac.uk
Bryan ZaldivarCSIC - IFIC (Valencia)E-mail confirmado em csic.es
Daniel Fernández-SánchezUniversidad Autónoma de MadridE-mail confirmado em uam.es
Novi QuadriantoProfessor of Machine Learning, University of Sussex UK, BCAM Spain, Monash IndonesiaE-mail confirmado em sussex.ac.uk
Mark RowlandResearch Scientist, Google DeepMindE-mail confirmado em google.com
Viktoriia SharmanskaImperial College Research FellowE-mail confirmado em imperial.ac.uk
Laura Cornejo-BuenoAssistant Professor, University of AlcaláE-mail confirmado em uah.es
Sancho Salcedo-SanzCatedrático de Teoría de la Señal, Universidad de AlcaláE-mail confirmado em uah.es
Lauwerys BUCB BiopharmaE-mail confirmado em ucb.com

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Daniel Hernández-Lobato

Associate Professor, Universidad Autónoma de Madrid

E-mail confirmado em uam.es - Página inicial

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


Título Ordenar por citações Ordenar por ano Ordenar por título	Citado por Citado por	Ano
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	405	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	315	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	288	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	284	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	283	2016
Generalized spike-and-slab priors for Bayesian group feature selection using expectation propagation D Hernández-Lobato, JM Hernández-Lobato, P Dupont The Journal of Machine Learning Research 14 (1), 1891-1945, 2013	119	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
Predictive entropy search for multi-objective bayesian optimization with constraints EC Garrido-Merchán, D Hernández-Lobato Neurocomputing 361, 50-68, 2019	96	2019
How large should ensembles of classifiers be? D Hernández-Lobato, G Martínez-Muñoz, A Suárez Pattern Recognition 46 (5), 1323-1336, 2013	95	2013
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	89	2018
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
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	81	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	57	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	49	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
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	44	2015
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
Expectation propagation for microarray data classification D Hernández-Lobato, JM Hernández-Lobato, A Suárez Pattern recognition letters 31 (12), 1618-1626, 2010	41	2010
Multi-class Gaussian process classification with noisy inputs C Villacampa-Calvo, B Zaldívar, EC Garrido-Merchán, ... Journal of Machine Learning Research 22 (36), 1-52, 2021	33	2021

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