Daniel Hernández-Lobato

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José Miguel Hernández-LobatoProfessor of Machine Learning, University of Cambridge確認したメールアドレス: cam.ac.uk
Alberto SuárezProfessor of Artificial Intelligence, Computer Science Department, Universidad Autónoma de Madrid確認したメールアドレス: uam.es
Gonzalo Martínez MuñozUniversidad Autónoma de Madrid確認したメールアドレス: uam.es
Eduardo C. Garrido-MerchánAssistant Professor of the Quantitative Methods Department, ICADE. Universidad Pontificia Comillas確認したメールアドレス: icade.comillas.edu
Richard E TurnerProfessor, University of Cambridge確認したメールアドレス: cam.ac.uk
Thang D BuiLecturer, Australian National University確認したメールアドレス: anu.edu.au
Yingzhen LiImperial College London確認したメールアドレス: imperial.ac.uk
Pierre DupontProfessor of Computer Science, ICTEAM, EPL, Université catholique de Louvain確認したメールアドレス: uclouvain.be
Ryan P. AdamsPrinceton University確認したメールアドレス: princeton.edu
Carlos Villacampa-Calvo確認したメールアドレス: uam.es
Simón Rodríguez SantanaEscuela Técnica Superior de Ingeniería (ICAI) - Universidad de Comillas確認したメールアドレス: comillas.edu
Amar ShahPhD student, University of Cambridge確認したメールアドレス: cam.ac.uk
Bryan ZaldivarCSIC - IFIC (Valencia)確認したメールアドレス: csic.es
Novi QuadriantoProfessor of Machine Learning, University of Sussex UK, BCAM Spain, Monash Indonesia確認したメールアドレス: sussex.ac.uk
Mark RowlandResearch Scientist, Google DeepMind確認したメールアドレス: google.com
Viktoriia SharmanskaImperial College Research Fellow確認したメールアドレス: imperial.ac.uk
Laura Cornejo-BuenoAssistant Professor, University of Alcalá確認したメールアドレス: uah.es
Sancho Salcedo-SanzCatedrático de Universidad (Full Professor), Universidad de Alcalá確認したメールアドレス: uah.es
Lauwerys BUCB Biopharma確認したメールアドレス: ucb.com
Daniel Fernández-SánchezUniversidad Autónoma de Madrid確認したメールアドレス: uam.es

フォロー

Daniel Hernández-Lobato

Associate Professor, Universidad Autónoma de Madrid

確認したメールアドレス: uam.es - ホームページ

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


タイトル引用回数順公開年順タイトル順	引用先引用先	年
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	392	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	277	2020
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	269	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	269	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	263	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	112	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	91	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	81	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	41	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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