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ORCIDDaniel Hernández-Lobato
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- affiliation: Universidad Autónoma de Madrid, Computer Science Department, Spain
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2020 – today
- 2024
[c33]Luis A. Ortega Andrés, Simón Rodríguez Santana, Daniel Hernández-Lobato:
Variational Linearized Laplace Approximation for Bayesian Deep Learning. ICML 2024- 2023
- [j25]Eduardo C. Garrido-Merchán
, Daniel Fernández-Sánchez, Daniel Hernández-Lobato:
Parallel predictive entropy search for multi-objective Bayesian optimization with constraints applied to the tuning of machine learning algorithms. Expert Syst. Appl. 215: 119328 (2023) - [j24]Daniel Fernández-Sánchez, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato:
Improved max-value entropy search for multi-objective bayesian optimization with constraints. Neurocomputing 546: 126290 (2023) - [j23]Bahram Jafrasteh, Daniel Hernández-Lobato, Simón Pedro Lubián-López, Isabel Benavente-Fernández:
Gaussian processes for missing value imputation. Knowl. Based Syst. 273: 110603 (2023) - [j22]Daniel Heestermans Svendsen, Daniel Hernández-Lobato, Luca Martino, Valero Laparra, Álvaro Moreno-Martínez, Gustau Camps-Valls:
Inference over radiative transfer models using variational and expectation maximization methods. Mach. Learn. 112(3): 921-937 (2023) - [c32]Luis A. Ortega, Simón Rodríguez Santana, Daniel Hernández-Lobato:
Deep Variational Implicit Processes. ICLR 2023 - [c31]Juan Maroñas, Daniel Hernández-Lobato:
Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification. ICML 2023: 24045-24081 - [i17]Luis A. Ortega, Simón Rodríguez Santana, Daniel Hernández-Lobato:
Variational Linearized Laplace Approximation for Bayesian Deep Learning. CoRR abs/2302.12565 (2023) - [i16]Francisco Javier Sáez-Maldonado, Juan Maroñas, Daniel Hernández-Lobato:
Deep Transformed Gaussian Processes. CoRR abs/2310.18230 (2023) - 2022
- [j21]Carlos Villacampa-Calvo, Gonzalo Hernández-Muñoz, Daniel Hernández-Lobato:
Alpha-divergence minimization for deep Gaussian processes. Int. J. Approx. Reason. 150: 139-171 (2022) - [j20]Simón Rodríguez Santana, Daniel Hernández-Lobato:
Adversarial α-divergence minimization for Bayesian approximate inference. Neurocomputing 471: 260-274 (2022) - [c30]Bahram Jafrasteh, Carlos Villacampa-Calvo, Daniel Hernández-Lobato:
Input Dependent Sparse Gaussian Processes. ICML 2022: 9739-9759 - [c29]Simón Rodríguez Santana, Bryan Zaldivar, Daniel Hernández-Lobato:
Function-space Inference with Sparse Implicit Processes. ICML 2022: 18723-18740 - [i15]Daniel Heestermans Svendsen, Daniel Hernández-Lobato, Luca Martino, Valero Laparra, Álvaro Moreno, Gustau Camps-Valls:
Inference over radiative transfer models using variational and expectation maximization methods. CoRR abs/2204.03346 (2022) - [i14]Bahram Jafrasteh, Daniel Hernández-Lobato, Simón Pedro Lubián-López, Isabel Benavente-Fernández:
Gaussian Processes for Missing Value Imputation. CoRR abs/2204.04648 (2022) - [i13]Juan Maroñas, Daniel Hernández-Lobato:
Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification. CoRR abs/2205.15008 (2022) - [i12]Luis A. Ortega, Simón Rodríguez Santana, Daniel Hernández-Lobato:
Deep Variational Implicit Processes. CoRR abs/2206.06720 (2022) - [i11]Simón Rodríguez Santana, Luis A. Ortega Andrés, Daniel Hernández-Lobato, Bryan Zaldivar:
Correcting Model Bias with Sparse Implicit Processes. CoRR abs/2207.10673 (2022) - 2021
- [j19]Carlos Villacampa-Calvo, Bryan Zaldivar, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato:
Multi-class Gaussian Process Classification with Noisy Inputs. J. Mach. Learn. Res. 22: 36:1-36:52 (2021) - [c28]Pablo Morales-Alvarez, Daniel Hernández-Lobato, Rafael Molina, José Miguel Hernández-Lobato:
Activation-level uncertainty in deep neural networks. ICLR 2021 - [i10]Bahram Jafrasteh, Carlos Villacampa-Calvo, Daniel Hernández-Lobato:
Input Dependent Sparse Gaussian Processes. CoRR abs/2107.07281 (2021) - [i9]Simón Rodríguez Santana, Bryan Zaldivar, Daniel Hernández-Lobato:
Sparse Implicit Processes for Approximate Inference. CoRR abs/2110.07618 (2021) - 2020
- [j18]Carlos Villacampa-Calvo, Daniel Hernández-Lobato:
Alpha divergence minimization in multi-class Gaussian process classification. Neurocomputing 378: 210-227 (2020) - [j17]Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato:
Dealing with categorical and integer-valued variables in Bayesian Optimization with Gaussian processes. Neurocomputing 380: 20-35 (2020) - [c27]Marta Gómez-Sancho, Daniel Hernández-Lobato:
Importance Weighted Adversarial Variational Bayes. HAIS 2020: 374-386 - [c26]Gonzalo Hernández-Muñoz, Carlos Villacampa-Calvo, Daniel Hernández-Lobato:
Deep Gaussian Processes Using Expectation Propagation and Monte Carlo Methods. ECML/PKDD (3) 2020: 479-494 - [i8]Carlos Villacampa-Calvo, Bryan Zaldivar, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato:
Multi-class Gaussian Process Classification with Noisy Inputs. CoRR abs/2001.10523 (2020) - [i7]Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato:
Parallel Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints. CoRR abs/2004.00601 (2020) - [i6]Daniel Fernández-Sánchez, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato:
Max-value Entropy Search for Multi-objective Bayesian Optimization with Constraints. CoRR abs/2011.01150 (2020)
2010 – 2019
- 2019
- [j16]Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato:
Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints. Neurocomputing 361: 50-68 (2019) - [p1]Daniel Hernández-Lobato, Pablo Morales-Mombiela, David Lopez-Paz, Alberto Suárez:
Non-linear Causal Inference Using Gaussianity Measures. Cause Effect Pairs in Machine Learning 2019: 257-299 - [i5]Simón Rodríguez Santana, Daniel Hernández-Lobato:
Adversarial α-divergence Minimization for Bayesian Approximate Inference. CoRR abs/1909.06945 (2019) - 2018
- [j15]Laura Cornejo-Bueno, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato, Sancho Salcedo-Sanz:
Bayesian optimization of a hybrid system for robust ocean wave features prediction. Neurocomputing 275: 818-828 (2018) - [c25]Irene Córdoba, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato, Concha Bielza, Pedro Larrañaga:
Bayesian Optimization of the PC Algorithm for Learning Gaussian Bayesian Networks. CAEPIA 2018: 44-54 - [i4]Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato:
Dealing with Categorical and Integer-valued Variables in Bayesian Optimization with Gaussian Processes. CoRR abs/1805.03463 (2018) - [i3]Irene Córdoba-Sánchez, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato, Concha Bielza, Pedro Larrañaga:
Bayesian optimization of the PC algorithm for learning Gaussian Bayesian networks. CoRR abs/1806.11015 (2018) - 2017
- [c24]Carlos Villacampa-Calvo, Daniel Hernández-Lobato:
Scalable Multi-Class Gaussian Process Classification using Expectation Propagation. ICML 2017: 3550-3559 - [c23]Laura Cornejo-Bueno, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato, Sancho Salcedo-Sanz:
Bayesian Optimization of a Hybrid Prediction System for Optimal Wave Energy Estimation Problems. IWANN (1) 2017: 648-660 - 2016
- [j14]Daniel Hernández-Lobato, Pablo Morales-Mombiela, David Lopez-Paz, Alberto Suárez:
Non-linear Causal Inference using Gaussianity Measures. J. Mach. Learn. Res. 17: 28:1-28:39 (2016) - [c22]Daniel Hernández-Lobato, José Miguel Hernández-Lobato:
Scalable Gaussian Process Classification via Expectation Propagation. AISTATS 2016: 168-176 - [c21]Viktoriia Sharmanska, Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Novi Quadrianto:
Ambiguity Helps: Classification with Disagreements in Crowdsourced Annotations. CVPR 2016: 2194-2202 - [c20]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 - [c19]Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Amar Shah, Ryan P. Adams:
Predictive Entropy Search for Multi-objective Bayesian Optimization. ICML 2016: 1492-1501 - [c18]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 - [i2]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
- [j13]Daniel Hernández-Lobato, Ioannis Katakis, Gonzalo Martínez-Muñoz, Ioannis Partalas:
Special Issue on "Solving complex machine learning problems with ensemble methods". Neurocomputing 150: 402-403 (2015) - [j12]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) - [c17]Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Zoubin Ghahramani:
A Probabilistic Model for Dirty Multi-task Feature Selection. ICML 2015: 1073-1082 - 2014
- [j11]Víctor Soto, Sergio García-Moratilla, Gonzalo Martínez-Muñoz, Daniel Hernández-Lobato, Alberto Suárez:
A Double Pruning Scheme for Boosting Ensembles. IEEE Trans. Cybern. 44(12): 2682-2695 (2014) - [c16]Daniel Hernández-Lobato, Viktoriia Sharmanska, Kristian Kersting, Christoph H. Lampert, Novi Quadrianto:
Mind the Nuisance: Gaussian Process Classification using Privileged Noise. NIPS 2014: 837-845 - [i1]Daniel Hernández-Lobato, Viktoriia Sharmanska, Kristian Kersting, Christoph H. Lampert, Novi Quadrianto:
Mind the Nuisance: Gaussian Process Classification using Privileged Noise. CoRR abs/1407.0179 (2014) - 2013
- [j10]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) - [j9]Daniel Hernández-Lobato, Gonzalo Martínez-Muñoz, Alberto Suárez:
How large should ensembles of classifiers be? Pattern Recognit. 46(5): 1323-1336 (2013) - [c15]Pablo Morales-Mombiela, Daniel Hernández-Lobato, Alberto Suárez:
Statistical Tests for the Detection of the Arrow of Time in Vector Autoregressive Models. IJCAI 2013: 1544-1550 - [c14]Daniel Hernández-Lobato, José Miguel Hernández-Lobato:
Learning Feature Selection Dependencies in Multi-task Learning. NIPS 2013: 746-754 - [c13]José Miguel Hernández-Lobato, James Robert Lloyd, Daniel Hernández-Lobato:
Gaussian Process Conditional Copulas with Applications to Financial Time Series. NIPS 2013: 1736-1744 - 2012
- [c12]Daniel Hernández-Lobato, Gonzalo Martínez-Muñoz, Alberto Suárez:
On the Independence of the Individual Predictions in Parallel Randomized Ensembles. ESANN 2012 - 2011
- [j8]Daniel Hernández-Lobato, Gonzalo Martínez-Muñoz, Alberto Suárez:
Empirical analysis and evaluation of approximate techniques for pruning regression bagging ensembles. Neurocomputing 74(12-13): 2250-2264 (2011) - [j7]José Miguel Hernández-Lobato, Daniel Hernández-Lobato, Alberto Suárez:
Network-based sparse Bayesian classification. Pattern Recognit. 44(4): 886-900 (2011) - [j6]Daniel Hernández-Lobato, Gonzalo Martínez-Muñoz, Alberto Suárez:
Inference on the prediction of ensembles of infinite size. Pattern Recognit. 44(7): 1426-1434 (2011) - [c11]Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Pierre Dupont:
Robust Multi-Class Gaussian Process Classification. NIPS 2011: 280-288 - 2010
- [j5]Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Alberto Suárez:
Expectation Propagation for microarray data classification. Pattern Recognit. Lett. 31(12): 1618-1626 (2010) - [c10]Víctor Soto, Gonzalo Martínez-Muñoz, Daniel Hernández-Lobato, Alberto Suárez:
A Double Pruning Algorithm for Classification Ensembles. MCS 2010: 104-113 - [c9]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
2000 – 2009
- 2009
- [j4]Gonzalo Martínez-Muñoz, Daniel Hernández-Lobato, Alberto Suárez:
An Analysis of Ensemble Pruning Techniques Based on Ordered Aggregation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2): 245-259 (2009) - [j3]Daniel Hernández-Lobato, Gonzalo Martínez-Muñoz, Alberto Suárez:
Statistical Instance-Based Pruning in Ensembles of Independent Classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 31(2): 364-369 (2009) - [c8]Gonzalo Martínez-Muñoz, Daniel Hernández-Lobato, Alberto Suárez:
Statistical Instance-Based Ensemble Pruning for Multi-class Problems. ICANN (1) 2009: 90-99 - 2008
- [j2]Gonzalo Martínez-Muñoz, Aitor Sánchez-Martínez, Daniel Hernández-Lobato, Alberto Suárez:
Class-switching neural network ensembles. Neurocomputing 71(13-15): 2521-2528 (2008) - [j1]Daniel Hernández-Lobato, José Miguel Hernández-Lobato:
Bayes Machines for binary classification. Pattern Recognit. Lett. 29(10): 1466-1473 (2008) - [c7]Daniel Hernández-Lobato:
Sparse Bayes Machines for Binary Classification. ICANN (1) 2008: 205-214 - 2007
- [c6]Gonzalo Martínez-Muñoz, Daniel Hernández-Lobato, Alberto Suárez:
Selection of Decision Stumps in Bagging Ensembles. ICANN (1) 2007: 319-328 - [c5]José Miguel Hernández-Lobato, Daniel Hernández-Lobato, Alberto Suárez:
GARCH Processes with Non-parametric Innovations for Market Risk Estimation. ICANN (2) 2007: 718-727 - [c4]Daniel Hernández-Lobato, Gonzalo Martínez-Muñoz, Alberto Suárez:
Out of Bootstrap Estimation of Generalization Error Curves in Bagging Ensembles. IDEAL 2007: 47-56 - 2006
- [c3]Gonzalo Martínez-Muñoz, Aitor Sánchez-Martínez, Daniel Hernández-Lobato, Alberto Suárez:
Building Ensembles of Neural Networks with Class-Switching. ICANN (1) 2006: 178-187 - [c2]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 - [c1]Daniel Hernández-Lobato, Gonzalo Martínez-Muñoz, Alberto Suárez:
Pruning in Ordered Regression Bagging Ensembles. IJCNN 2006: 1266-1273
Coauthor Index
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