Alberto Suarez, Ph.D.
Affiliations: | Universidad Autónoma de Madrid, Madrid, Comunidad de Madrid, Spain |
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Publications
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| Torrecilla JL, Ramos-Carreño C, Sánchez-Montañés M, et al. (2020) Optimal classification of Gaussian processes in homo- and heteroscedastic settings Statistics and Computing. 30: 1091-1111 |
| Sabzevari M, Martínez-Muñoz G, Suárez A. (2018) Vote-boosting ensembles Pattern Recognition. 83: 119-133 |
| Sabzevari M, Martínez-Muñoz G, Suárez A. (2018) A two-stage ensemble method for the detection of class-label noise Neurocomputing. 275: 2374-2383 |
| Jafrasteh B, Fathianpour N, Suárez A. (2018) Comparison of machine learning methods for copper ore grade estimation Computational Geosciences. 22: 1371-1388 |
| Hernández-Lobato D, Morales-Mombiela P, Lopez-Paz D, et al. (2016) Non-linear causal inference using Gaussianity measures Journal of Machine Learning Research. 17: 939-977 |
| Sabzevari M, Martínez-Muñoz G, Suárez A. (2015) Small margin ensembles can be robust to class-label noise Neurocomputing. 160: 18-33 |
| Hernández-Lobato JM, Hernández-Lobato D, Suárez A. (2015) Expectation propagation in linear regression models with spike-and-slab priors Machine Learning. 99: 437-487 |
| Soto V, García-Moratilla S, Martínez-Muñoz G, et al. (2014) A double pruning scheme for boosting ensembles. Ieee Transactions On Cybernetics. 44: 2682-95 |
| Hernández L, Tejero J, Suárez A, et al. (2014) Percentiles of sums of heavy-tailed random variables: Beyond the single-loss approximation. Statistics and Computing. 24: 377-397 |
| HernáNdez-Lobato D, MartíNez-MuñOz G, SuáRez A. (2013) How large should ensembles of classifiers be Pattern Recognition. 46: 1323-1336 |