| 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 | 390 | 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 | 266 | 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 | 265 | 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 | 264 | 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 | 256 | 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 | 111 | 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 | 95 | 2013 |
| Predictive entropy search for multi-objective bayesian optimization with constraints EC Garrido-Merchán, D Hernández-Lobato Neurocomputing 361, 50-68, 2019 | 86 | 2019 |
| 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 |
| 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 | 79 | 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 |
| Scalable Gaussian process classification via expectation propagation D Hernández-Lobato, JM Hernández-Lobato Artificial Intelligence and Statistics, 168-176, 2016 | 64 | 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 | 45 | 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 | 40 | 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 | 40 | 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 | 33 | 2013 |
José Miguel Hernández-LobatoProfessor of Machine Learning, University of Cambridge在 cam.ac.uk 的电子邮件经过验证
