Daniel Hernández-Lobato
Cited by
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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
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
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
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
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
How large should ensembles of classifiers be?
D Hernández-Lobato, G Martínez-Muñoz, A Suárez
Pattern Recognition 46 (5), 1323-1336, 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
Dealing with categorical and integer-valued variables in bayesian optimization with gaussian processes
EC Garrido-Merchán, D Hernández-Lobato
Neurocomputing 380, 20-35, 2020
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
Scalable Gaussian process classification via expectation propagation
D Hernández-Lobato, JM Hernández-Lobato
Artificial Intelligence and Statistics, 168-176, 2016
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
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
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
Mind the nuisance: Gaussian process classification using privileged noise
D Hernández-Lobato, V Sharmanska, K Kersting, CH Lampert, ...
arXiv preprint arXiv:1407.0179, 2014
Expectation propagation for microarray data classification
D Hernández-Lobato, JM Hernández-Lobato, A Suárez
Pattern recognition letters 31 (12), 1618-1626, 2010
Class-switching neural network ensembles
G Martínez-Muñoz, A Sánchez-Martínez, D Hernández-Lobato, A Suárez
Neurocomputing 71 (13-15), 2521-2528, 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
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
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
Learning feature selection dependencies in multi-task learning
D Hernández-Lobato, JM Hernández-Lobato
NIPS, 2013
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