Daniel Hernández-Lobato

Publication Activity (10 Years)

Years Active: 2006-2023
Publications (10 Years): 43

Top Topics

Gaussian Process

Linear Regression Models

Top Venues

Publications

Luis A. Ortega, Simón Rodríguez Santana, Daniel Hernández-Lobato
Variational Linearized Laplace Approximation for Bayesian Deep Learning. CoRR (2023)
Daniel Fernández-Sánchez, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato
Improved max-value entropy search for multi-objective bayesian optimization with constraints. Neurocomputing 546 (2023)
Francisco Javier Sáez-Maldonado, Juan Maroñas, Daniel Hernández-Lobato
Deep Transformed Gaussian Processes. CoRR (2023)
Juan Maroñas, Daniel Hernández-Lobato
Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification. ICML (2023)
Luis A. Ortega, Simón Rodríguez Santana, Daniel Hernández-Lobato
Deep Variational Implicit Processes. ICLR (2023)
Bahram Jafrasteh, Daniel Hernández-Lobato, Simón Pedro Lubián-López, Isabel Benavente-Fernández
Gaussian processes for missing value imputation. Knowl. Based Syst. 273 (2023)
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) (2023)
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 (2023)
Bahram Jafrasteh, Carlos Villacampa-Calvo, Daniel Hernández-Lobato
Input Dependent Sparse Gaussian Processes. ICML (2022)
Simón Rodríguez Santana, Luis A. Ortega Andrés, Daniel Hernández-Lobato, Bryan Zaldivar
Correcting Model Bias with Sparse Implicit Processes. CoRR (2022)
Luis A. Ortega, Simón Rodríguez Santana, Daniel Hernández-Lobato
Deep Variational Implicit Processes. CoRR (2022)
Simón Rodríguez Santana, Bryan Zaldivar, Daniel Hernández-Lobato
Function-space Inference with Sparse Implicit Processes. ICML (2022)
Carlos Villacampa-Calvo, Gonzalo Hernández-Muñoz, Daniel Hernández-Lobato
Alpha-divergence minimization for deep Gaussian processes. Int. J. Approx. Reason. 150 (2022)
Bahram Jafrasteh, Daniel Hernández-Lobato, Simón Pedro Lubián-López, Isabel Benavente-Fernández
Gaussian Processes for Missing Value Imputation. CoRR (2022)
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 (2022)
Simón Rodríguez Santana, Daniel Hernández-Lobato
Adversarial α-divergence minimization for Bayesian approximate inference. Neurocomputing 471 (2022)
Juan Maroñas, Daniel Hernández-Lobato
Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification. CoRR (2022)
Pablo Morales-Alvarez, Daniel Hernández-Lobato, Rafael Molina, José Miguel Hernández-Lobato
Activation-level uncertainty in deep neural networks. ICLR (2021)
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 (2021)
Simón Rodríguez Santana, Bryan Zaldivar, Daniel Hernández-Lobato
Sparse Implicit Processes for Approximate Inference. CoRR (2021)
Bahram Jafrasteh, Carlos Villacampa-Calvo, Daniel Hernández-Lobato
Input Dependent Sparse Gaussian Processes. CoRR (2021)
Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato
Parallel Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints. CoRR (2020)
Carlos Villacampa-Calvo, Bryan Zaldivar, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato
Multi-class Gaussian Process Classification with Noisy Inputs. CoRR (2020)
Daniel Fernández-Sánchez, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato
Max-value Entropy Search for Multi-objective Bayesian Optimization with Constraints. CoRR (2020)
Marta Gómez-Sancho, Daniel Hernández-Lobato
Importance Weighted Adversarial Variational Bayes. HAIS (2020)
Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato
Dealing with categorical and integer-valued variables in Bayesian Optimization with Gaussian processes. Neurocomputing 380 (2020)
Gonzalo Hernández-Muñoz, Carlos Villacampa-Calvo, Daniel Hernández-Lobato
Deep Gaussian Processes Using Expectation Propagation and Monte Carlo Methods. ECML/PKDD (3) (2020)
Carlos Villacampa-Calvo, Daniel Hernández-Lobato
Alpha divergence minimization in multi-class Gaussian process classification. Neurocomputing 378 (2020)
Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato
Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints. Neurocomputing 361 (2019)
Simón Rodríguez Santana, Daniel Hernández-Lobato
Adversarial α-divergence Minimization for Bayesian Approximate Inference. CoRR (2019)
Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato
Dealing with Categorical and Integer-valued Variables in Bayesian Optimization with Gaussian Processes. CoRR (2018)
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 (2018)
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 (2018)
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)
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)
Carlos Villacampa-Calvo, Daniel Hernández-Lobato
Scalable Multi-Class Gaussian Process Classification using Expectation Propagation. ICML (2017)
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 (2016)
Viktoriia Sharmanska, Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Novi Quadrianto
Ambiguity Helps: Classification with Disagreements in Crowdsourced Annotations. CVPR (2016)
Daniel Hernández-Lobato, José Miguel Hernández-Lobato
Scalable Gaussian Process Classification via Expectation Propagation. AISTATS (2016)
Daniel Hernández-Lobato, Pablo Morales-Mombiela, David Lopez-Paz, Alberto Suárez
Non-linear Causal Inference using Gaussianity Measures. J. Mach. Learn. Res. 17 (2016)
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)
José Miguel Hernández-Lobato, Yingzhen Li, Mark Rowland, Thang D. Bui, Daniel Hernández-Lobato, Richard E. Turner
Black-Box Alpha Divergence Minimization. ICML (2016)
Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Amar Shah, Ryan P. Adams
Predictive Entropy Search for Multi-objective Bayesian Optimization. ICML (2016)
Daniel Hernández-Lobato, Ioannis Katakis, Gonzalo Martínez-Muñoz, Ioannis Partalas
Special Issue on "Solving complex machine learning problems with ensemble methods". Neurocomputing 150 (2015)
José Miguel Hernández-Lobato, Daniel Hernández-Lobato, Alberto Suárez
Expectation propagation in linear regression models with spike-and-slab priors. Mach. Learn. 99 (3) (2015)
Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Zoubin Ghahramani
A Probabilistic Model for Dirty Multi-task Feature Selection. ICML (2015)
Daniel Hernández-Lobato, Viktoriia Sharmanska, Kristian Kersting, Christoph H. Lampert, Novi Quadrianto
Mind the Nuisance: Gaussian Process Classification using Privileged Noise. NIPS (2014)
Daniel Hernández-Lobato, Viktoriia Sharmanska, Kristian Kersting, Christoph H. Lampert, Novi Quadrianto
Mind the Nuisance: Gaussian Process Classification using Privileged Noise. CoRR (2014)
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) (2014)
Daniel Hernández-Lobato, José Miguel Hernández-Lobato
Learning Feature Selection Dependencies in Multi-task Learning. NIPS (2013)
Daniel Hernández-Lobato, Gonzalo Martínez-Muñoz, Alberto Suárez
How large should ensembles of classifiers be? Pattern Recognit. 46 (5) (2013)
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) (2013)
Pablo Morales-Mombiela, Daniel Hernández-Lobato, Alberto Suárez
Statistical Tests for the Detection of the Arrow of Time in Vector Autoregressive Models. IJCAI (2013)
José Miguel Hernández-Lobato, James Robert Lloyd, Daniel Hernández-Lobato
Gaussian Process Conditional Copulas with Applications to Financial Time Series. NIPS (2013)
Daniel Hernández-Lobato, Gonzalo Martínez-Muñoz, Alberto Suárez
On the Independence of the Individual Predictions in Parallel Randomized Ensembles. ESANN (2012)
Daniel Hernández-Lobato, Gonzalo Martínez-Muñoz, Alberto Suárez
Inference on the prediction of ensembles of infinite size. Pattern Recognit. 44 (7) (2011)
Daniel Hernández-Lobato, Gonzalo Martínez-Muñoz, Alberto Suárez
Empirical analysis and evaluation of approximate techniques for pruning regression bagging ensembles. Neurocomputing 74 (12-13) (2011)
Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Pierre Dupont
Robust Multi-Class Gaussian Process Classification. NIPS (2011)
José Miguel Hernández-Lobato, Daniel Hernández-Lobato, Alberto Suárez
Network-based sparse Bayesian classification. Pattern Recognit. 44 (4) (2011)
Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Alberto Suárez
Expectation Propagation for microarray data classification. Pattern Recognit. Lett. 31 (12) (2010)
Víctor Soto, Gonzalo Martínez-Muñoz, Daniel Hernández-Lobato, Alberto Suárez
A Double Pruning Algorithm for Classification Ensembles. MCS (2010)
Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Thibault Helleputte, Pierre Dupont
Expectation Propagation for Bayesian Multi-task Feature Selection. ECML/PKDD (1) (2010)
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) (2009)
Gonzalo Martínez-Muñoz, Daniel Hernández-Lobato, Alberto Suárez
Statistical Instance-Based Ensemble Pruning for Multi-class Problems. ICANN (1) (2009)
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) (2009)
Gonzalo Martínez-Muñoz, Aitor Sánchez-Martínez, Daniel Hernández-Lobato, Alberto Suárez
Class-switching neural network ensembles. Neurocomputing 71 (13-15) (2008)
Daniel Hernández-Lobato
Sparse Bayes Machines for Binary Classification. ICANN (1) (2008)
Daniel Hernández-Lobato, José Miguel Hernández-Lobato
Bayes Machines for binary classification. Pattern Recognit. Lett. 29 (10) (2008)
Gonzalo Martínez-Muñoz, Daniel Hernández-Lobato, Alberto Suárez
Selection of Decision Stumps in Bagging Ensembles. ICANN (1) (2007)
Daniel Hernández-Lobato, Gonzalo Martínez-Muñoz, Alberto Suárez
Out of Bootstrap Estimation of Generalization Error Curves in Bagging Ensembles. IDEAL (2007)
José Miguel Hernández-Lobato, Daniel Hernández-Lobato, Alberto Suárez
GARCH Processes with Non-parametric Innovations for Market Risk Estimation. ICANN (2) (2007)
Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Rubén Ruiz-Torrubiano, Ángel Valle
Pruning Adaptive Boosting Ensembles by Means of a Genetic Algorithm. IDEAL (2006)
Gonzalo Martínez-Muñoz, Aitor Sánchez-Martínez, Daniel Hernández-Lobato, Alberto Suárez
Building Ensembles of Neural Networks with Class-Switching. ICANN (1) (2006)
Daniel Hernández-Lobato, Gonzalo Martínez-Muñoz, Alberto Suárez
Pruning in Ordered Regression Bagging Ensembles. IJCNN (2006)