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José Miguel Hernández-Lobato
Publications
- 2021
- [c69]Pablo Morales-Alvarez, Daniel Hernández-Lobato, Rafael Molina, José Miguel Hernández-Lobato:
Activation-level uncertainty in deep neural networks. ICLR 2021 - 2016
- [c30]Daniel Hernández-Lobato, José Miguel Hernández-Lobato:
Scalable Gaussian Process Classification via Expectation Propagation. AISTATS 2016: 168-176 - [c29]Viktoriia Sharmanska, Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Novi Quadrianto:
Ambiguity Helps: Classification with Disagreements in Crowdsourced Annotations. CVPR 2016: 2194-2202 - [c28]Thang D. Bui, Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Yingzhen Li, Richard E. Turner:
Deep Gaussian Processes for Regression using Approximate Expectation Propagation. ICML 2016: 1472-1481 - [c27]Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Amar Shah, Ryan P. Adams:
Predictive Entropy Search for Multi-objective Bayesian Optimization. ICML 2016: 1492-1501 - [c26]José Miguel Hernández-Lobato, Yingzhen Li, Mark Rowland, Thang D. Bui, Daniel Hernández-Lobato, Richard E. Turner:
Black-Box Alpha Divergence Minimization. ICML 2016: 1511-1520 - [i7]Thang D. Bui, Daniel Hernández-Lobato, Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner:
Deep Gaussian Processes for Regression using Approximate Expectation Propagation. CoRR abs/1602.04133 (2016) - 2015
- [j6]José Miguel Hernández-Lobato, Daniel Hernández-Lobato, Alberto Suárez:
Expectation propagation in linear regression models with spike-and-slab priors. Mach. Learn. 99(3): 437-487 (2015) - [c24]Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Zoubin Ghahramani:
A Probabilistic Model for Dirty Multi-task Feature Selection. ICML 2015: 1073-1082 - 2013
- [j5]Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Pierre Dupont:
Generalized spike-and-slab priors for Bayesian group feature selection using expectation propagation. J. Mach. Learn. Res. 14(1): 1891-1945 (2013) - [c12]Daniel Hernández-Lobato, José Miguel Hernández-Lobato:
Learning Feature Selection Dependencies in Multi-task Learning. NIPS 2013: 746-754 - [c11]José Miguel Hernández-Lobato, James Robert Lloyd, Daniel Hernández-Lobato:
Gaussian Process Conditional Copulas with Applications to Financial Time Series. NIPS 2013: 1736-1744 - 2011
- [j3]José Miguel Hernández-Lobato, Daniel Hernández-Lobato, Alberto Suárez:
Network-based sparse Bayesian classification. Pattern Recognit. 44(4): 886-900 (2011) - [c7]Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Pierre Dupont:
Robust Multi-Class Gaussian Process Classification. NIPS 2011: 280-288 - 2010
- [j2]Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Alberto Suárez:
Expectation Propagation for microarray data classification. Pattern Recognit. Lett. 31(12): 1618-1626 (2010) - [c5]Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Thibault Helleputte, Pierre Dupont:
Expectation Propagation for Bayesian Multi-task Feature Selection. ECML/PKDD (1) 2010: 522-537 - 2008
- [j1]Daniel Hernández-Lobato, José Miguel Hernández-Lobato:
Bayes Machines for binary classification. Pattern Recognit. Lett. 29(10): 1466-1473 (2008) - 2007
- [c4]José Miguel Hernández-Lobato, Daniel Hernández-Lobato, Alberto Suárez:
GARCH Processes with Non-parametric Innovations for Market Risk Estimation. ICANN (2) 2007: 718-727 - 2006
- [c1]Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Rubén Ruiz-Torrubiano, Ángel Valle:
Pruning Adaptive Boosting Ensembles by Means of a Genetic Algorithm. IDEAL 2006: 322-329
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