Daniel Hernández-Lobato

According to our database, Daniel Hernández-Lobato authored at least 64 papers between 2006 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Variational Linearized Laplace Approximation for Bayesian Deep Learning.

Luis A. Ortega Andrés, Simón Rodríguez Santana, Daniel Hernández-Lobato

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Parallel predictive entropy search for multi-objective Bayesian optimization with constraints applied to the tuning of machine learning algorithms.

Eduardo C. Garrido-Merchán, Daniel Fernández-Sánchez, Daniel Hernández-Lobato

Expert Syst. Appl., April, 2023

Inference over radiative transfer models using variational and expectation maximization methods.

Daniel Heestermans Svendsen, Daniel Hernández-Lobato, Luca Martino, Valero Laparra, Álvaro Moreno-Martínez, Gustau Camps-Valls

Mach. Learn., March, 2023

Gaussian processes for missing value imputation.

Bahram Jafrasteh, Daniel Hernández-Lobato, Simón Pedro Lubián-López, Isabel Benavente-Fernández

Knowl. Based Syst., 2023

Improved max-value entropy search for multi-objective bayesian optimization with constraints.

Daniel Fernández-Sánchez, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato

Neurocomputing, 2023

Deep Transformed Gaussian Processes.

Francisco Javier Sáez-Maldonado, Juan Maroñas, Daniel Hernández-Lobato

CoRR, 2023

Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification.

Juan Maroñas, Daniel Hernández-Lobato

Proceedings of the International Conference on Machine Learning, 2023

Deep Variational Implicit Processes.

Luis A. Ortega, Simón Rodríguez Santana, Daniel Hernández-Lobato

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Adversarial α-divergence minimization for Bayesian approximate inference.

Simón Rodríguez Santana, Daniel Hernández-Lobato

Neurocomputing, 2022

Alpha-divergence minimization for deep Gaussian processes.

Carlos Villacampa-Calvo, Gonzalo Hernández-Muñoz, Daniel Hernández-Lobato

Int. J. Approx. Reason., 2022

Correcting Model Bias with Sparse Implicit Processes.

Simón Rodríguez Santana, Luis A. Ortega Andrés, Daniel Hernández-Lobato, Bryan Zaldivar

CoRR, 2022

Function-space Inference with Sparse Implicit Processes.

Simón Rodríguez Santana, Bryan Zaldivar, Daniel Hernández-Lobato

Proceedings of the International Conference on Machine Learning, 2022

Input Dependent Sparse Gaussian Processes.

Bahram Jafrasteh, Carlos Villacampa-Calvo, Daniel Hernández-Lobato

Proceedings of the International Conference on Machine Learning, 2022

2021

Multi-class Gaussian Process Classification with Noisy Inputs.

Carlos Villacampa-Calvo, Bryan Zaldivar, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato

J. Mach. Learn. Res., 2021

Sparse Implicit Processes for Approximate Inference.

Simón Rodríguez Santana, Bryan Zaldivar, Daniel Hernández-Lobato

CoRR, 2021

Activation-level uncertainty in deep neural networks.

Pablo Morales-Alvarez, Daniel Hernández-Lobato, Rafael Molina, José Miguel Hernández-Lobato

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Alpha divergence minimization in multi-class Gaussian process classification.

Carlos Villacampa-Calvo, Daniel Hernández-Lobato

Neurocomputing, 2020

Dealing with categorical and integer-valued variables in Bayesian Optimization with Gaussian processes.

Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato

Neurocomputing, 2020

Max-value Entropy Search for Multi-objective Bayesian Optimization with Constraints.

Daniel Fernández-Sánchez, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato

CoRR, 2020

Parallel Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints.

Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato

CoRR, 2020

Deep Gaussian Processes Using Expectation Propagation and Monte Carlo Methods.

Gonzalo Hernández-Muñoz, Carlos Villacampa-Calvo, Daniel Hernández-Lobato

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2020

Importance Weighted Adversarial Variational Bayes.

Marta Gómez-Sancho, Daniel Hernández-Lobato

Proceedings of the Hybrid Artificial Intelligent Systems - 15th International Conference, 2020

2019

Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints.

Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato

Neurocomputing, 2019

Non-linear Causal Inference Using Gaussianity Measures.

Daniel Hernández-Lobato, Pablo Morales-Mombiela, David Lopez-Paz, Alberto Suárez

Proceedings of the Cause Effect Pairs in Machine Learning, 2019

2018

Bayesian optimization of a hybrid system for robust ocean wave features prediction.

Laura Cornejo-Bueno, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato, Sancho Salcedo-Sanz

Neurocomputing, 2018

Bayesian Optimization of the PC Algorithm for Learning Gaussian Bayesian Networks.

Irene Córdoba, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato, Concha Bielza, Pedro Larrañaga

Proceedings of the Advances in Artificial Intelligence, 2018

2017

Bayesian Optimization of a Hybrid Prediction System for Optimal Wave Energy Estimation Problems.

Laura Cornejo-Bueno, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato, Sancho Salcedo-Sanz

Proceedings of the Advances in Computational Intelligence, 2017

Scalable Multi-Class Gaussian Process Classification using Expectation Propagation.

Carlos Villacampa-Calvo, Daniel Hernández-Lobato

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Non-linear Causal Inference using Gaussianity Measures.

Daniel Hernández-Lobato, Pablo Morales-Mombiela, David Lopez-Paz, Alberto Suárez

J. Mach. Learn. Res., 2016

Black-Box Alpha Divergence Minimization.

José Miguel Hernández-Lobato, Yingzhen Li, Mark Rowland, Thang D. Bui, Daniel Hernández-Lobato, Richard E. Turner

Proceedings of the 33nd International Conference on Machine Learning, 2016

Predictive Entropy Search for Multi-objective Bayesian Optimization.

Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Amar Shah, Ryan P. Adams

Proceedings of the 33nd International Conference on Machine Learning, 2016

Deep Gaussian Processes for Regression using Approximate Expectation Propagation.

Thang D. Bui, Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Yingzhen Li, Richard E. Turner

Proceedings of the 33nd International Conference on Machine Learning, 2016

Ambiguity Helps: Classification with Disagreements in Crowdsourced Annotations.

Viktoriia Sharmanska, Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Novi Quadrianto

Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016

Scalable Gaussian Process Classification via Expectation Propagation.

Daniel Hernández-Lobato, José Miguel Hernández-Lobato

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

Expectation propagation in linear regression models with spike-and-slab priors.

José Miguel Hernández-Lobato, Daniel Hernández-Lobato, Alberto Suárez

Mach. Learn., 2015

Special Issue on "Solving complex machine learning problems with ensemble methods".

Daniel Hernández-Lobato, Ioannis Katakis, Gonzalo Martínez-Muñoz, Ioannis Partalas

Neurocomputing, 2015

A Probabilistic Model for Dirty Multi-task Feature Selection.

Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Zoubin Ghahramani

Proceedings of the 32nd International Conference on Machine Learning, 2015

2014

A Double Pruning Scheme for Boosting Ensembles.

Víctor Soto, Sergio García-Moratilla, Gonzalo Martínez-Muñoz, Daniel Hernández-Lobato, Alberto Suárez

IEEE Trans. Cybern., 2014

Mind the Nuisance: Gaussian Process Classification using Privileged Noise.

Daniel Hernández-Lobato, Viktoriia Sharmanska, Kristian Kersting, Christoph H. Lampert, Novi Quadrianto

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

2013

How large should ensembles of classifiers be?

Daniel Hernández-Lobato, Gonzalo Martínez-Muñoz, Alberto Suárez

Pattern Recognit., 2013

Generalized spike-and-slab priors for Bayesian group feature selection using expectation propagation.

Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Pierre Dupont

J. Mach. Learn. Res., 2013

Gaussian Process Conditional Copulas with Applications to Financial Time Series.

José Miguel Hernández-Lobato, James Robert Lloyd, Daniel Hernández-Lobato

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Learning Feature Selection Dependencies in Multi-task Learning.

Daniel Hernández-Lobato, José Miguel Hernández-Lobato

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Statistical Tests for the Detection of the Arrow of Time in Vector Autoregressive Models.

Pablo Morales-Mombiela, Daniel Hernández-Lobato, Alberto Suárez

Proceedings of the IJCAI 2013, 2013

2012

On the Independence of the Individual Predictions in Parallel Randomized Ensembles.

Daniel Hernández-Lobato, Gonzalo Martínez-Muñoz, Alberto Suárez

Proceedings of the 20th European Symposium on Artificial Neural Networks, 2012

2011

Inference on the prediction of ensembles of infinite size.

Daniel Hernández-Lobato, Gonzalo Martínez-Muñoz, Alberto Suárez

Pattern Recognit., 2011

Network-based sparse Bayesian classification.

José Miguel Hernández-Lobato, Daniel Hernández-Lobato, Alberto Suárez

Pattern Recognit., 2011

Empirical analysis and evaluation of approximate techniques for pruning regression bagging ensembles.

Daniel Hernández-Lobato, Gonzalo Martínez-Muñoz, Alberto Suárez

Neurocomputing, 2011

Robust Multi-Class Gaussian Process Classification.

Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Pierre Dupont

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

2010

Expectation Propagation for microarray data classification.

Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Alberto Suárez

Pattern Recognit. Lett., 2010

Expectation Propagation for Bayesian Multi-task Feature Selection.

Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Thibault Helleputte, Pierre Dupont

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2010

A Double Pruning Algorithm for Classification Ensembles.

Víctor Soto, Gonzalo Martínez-Muñoz, Daniel Hernández-Lobato, Alberto Suárez

Proceedings of the Multiple Classifier Systems, 9th International Workshop, 2010

2009

An Analysis of Ensemble Pruning Techniques Based on Ordered Aggregation.

Gonzalo Martínez-Muñoz, Daniel Hernández-Lobato, Alberto Suárez

IEEE Trans. Pattern Anal. Mach. Intell., 2009

Statistical Instance-Based Pruning in Ensembles of Independent Classifiers.

Daniel Hernández-Lobato, Gonzalo Martínez-Muñoz, Alberto Suárez

IEEE Trans. Pattern Anal. Mach. Intell., 2009

Statistical Instance-Based Ensemble Pruning for Multi-class Problems.

Gonzalo Martínez-Muñoz, Daniel Hernández-Lobato, Alberto Suárez

Proceedings of the Artificial Neural Networks, 2009

2008

Bayes Machines for binary classification.

Daniel Hernández-Lobato, José Miguel Hernández-Lobato

Pattern Recognit. Lett., 2008

Class-switching neural network ensembles.

Gonzalo Martínez-Muñoz, Aitor Sánchez-Martínez, Daniel Hernández-Lobato, Alberto Suárez

Neurocomputing, 2008

Sparse Bayes Machines for Binary Classification.

Daniel Hernández-Lobato

Proceedings of the Artificial Neural Networks, 2008

2007

Out of Bootstrap Estimation of Generalization Error Curves in Bagging Ensembles.

Daniel Hernández-Lobato, Gonzalo Martínez-Muñoz, Alberto Suárez

Proceedings of the Intelligent Data Engineering and Automated Learning, 2007

Selection of Decision Stumps in Bagging Ensembles.

Gonzalo Martínez-Muñoz, Daniel Hernández-Lobato, Alberto Suárez

Proceedings of the Artificial Neural Networks, 2007

GARCH Processes with Non-parametric Innovations for Market Risk Estimation.

José Miguel Hernández-Lobato, Daniel Hernández-Lobato, Alberto Suárez

Proceedings of the Artificial Neural Networks, 2007

2006

Pruning in Ordered Regression Bagging Ensembles.

Daniel Hernández-Lobato, Gonzalo Martínez-Muñoz, Alberto Suárez

Proceedings of the International Joint Conference on Neural Networks, 2006

Pruning Adaptive Boosting Ensembles by Means of a Genetic Algorithm.

Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Rubén Ruiz-Torrubiano, Ángel Valle

Proceedings of the Intelligent Data Engineering and Automated Learning, 2006

Building Ensembles of Neural Networks with Class-Switching.

Gonzalo Martínez-Muñoz, Aitor Sánchez-Martínez, Daniel Hernández-Lobato, Alberto Suárez

Proceedings of the Artificial Neural Networks, 2006