Building Ensembles of Neural Networks with Class-Switching. - Gonzalo Martínez-Muñoz; Aitor Sánchez-Martínez; Daniel Hernández-Lobato; Alberto Suárez 001 (ICANN (1), 2006)

Pruning Adaptive Boosting Ensembles by Means of a Genetic Algorithm. - Daniel Hernández-Lobato; José Miguel Hernández-Lobato; Rubén Ruiz-Torrubiano; Ángel Valle (IDEAL, 2006)

Pruning in Ordered Regression Bagging Ensembles. - Daniel Hernández-Lobato; Gonzalo Martínez-Muñoz; Alberto Suárez 001 (IJCNN, 2006)

GARCH Processes with Non-parametric Innovations for Market Risk Estimation. - José Miguel Hernández-Lobato; Daniel Hernández-Lobato; Alberto Suárez 001 (ICANN (2), 2007)

Selection of Decision Stumps in Bagging Ensembles. - Gonzalo Martínez-Muñoz; Daniel Hernández-Lobato; Alberto Suárez 001 (ICANN (1), 2007)

Out of Bootstrap Estimation of Generalization Error Curves in Bagging Ensembles. - Daniel Hernández-Lobato; Gonzalo Martínez-Muñoz; Alberto Suárez 001 (IDEAL, 2007)

Sparse Bayes Machines for Binary Classification. - Daniel Hernández-Lobato (ICANN (1), 2008)

Class-switching neural network ensembles. - Gonzalo Martínez-Muñoz; Aitor Sánchez-Martínez; Daniel Hernández-Lobato; Alberto Suárez 001 (Neurocomputing, 2008)

Bayes Machines for binary classification. - Daniel Hernández-Lobato; José Miguel Hernández-Lobato (Pattern Recognit. Lett., 2008)

Statistical Instance-Based Ensemble Pruning for Multi-class Problems. - Gonzalo Martínez-Muñoz; Daniel Hernández-Lobato; Alberto Suárez 001 (ICANN (1), 2009)

Statistical Instance-Based Pruning in Ensembles of Independent Classifiers. - Daniel Hernández-Lobato; Gonzalo Martínez-Muñoz; Alberto Suárez 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2009)

An Analysis of Ensemble Pruning Techniques Based on Ordered Aggregation. - Gonzalo Martínez-Muñoz; Daniel Hernández-Lobato; Alberto Suárez 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2009)

A Double Pruning Algorithm for Classification Ensembles. - Víctor Soto; Gonzalo Martínez-Muñoz; Daniel Hernández-Lobato; Alberto Suárez 001 (MCS, 2010)

Expectation Propagation for Bayesian Multi-task Feature Selection. - Daniel Hernández-Lobato; José Miguel Hernández-Lobato; Thibault Helleputte; Pierre Dupont (ECML/PKDD (1), 2010)

Expectation Propagation for microarray data classification. - Daniel Hernández-Lobato; José Miguel Hernández-Lobato; Alberto Suárez 001 (Pattern Recognit. Lett., 2010)

Robust Multi-Class Gaussian Process Classification. - Daniel Hernández-Lobato; José Miguel Hernández-Lobato; Pierre Dupont (NIPS, 2011)

Empirical analysis and evaluation of approximate techniques for pruning regression bagging ensembles. - Daniel Hernández-Lobato; Gonzalo Martínez-Muñoz; Alberto Suárez 001 (Neurocomputing, 2011)

Network-based sparse Bayesian classification. - José Miguel Hernández-Lobato; Daniel Hernández-Lobato; Alberto Suárez 001 (Pattern Recognit., 2011)

Inference on the prediction of ensembles of infinite size. - Daniel Hernández-Lobato; Gonzalo Martínez-Muñoz; Alberto Suárez 001 (Pattern Recognit., 2011)

On the Independence of the Individual Predictions in Parallel Randomized Ensembles. - Daniel Hernández-Lobato; Gonzalo Martínez-Muñoz; Alberto Suárez 001 (ESANN, 2012)

Statistical Tests for the Detection of the Arrow of Time in Vector Autoregressive Models. - Pablo Morales-Mombiela; Daniel Hernández-Lobato; Alberto Suárez 001 (IJCAI, 2013)

Learning Feature Selection Dependencies in Multi-task Learning. - Daniel Hernández-Lobato; José Miguel Hernández-Lobato (NIPS, 2013)

Gaussian Process Conditional Copulas with Applications to Financial Time Series. - José Miguel Hernández-Lobato; James Robert Lloyd; Daniel Hernández-Lobato (NIPS, 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)

How large should ensembles of classifiers be? - Daniel Hernández-Lobato; Gonzalo Martínez-Muñoz; Alberto Suárez 001 (Pattern Recognit., 2013)

Mind the Nuisance: Gaussian Process Classification using Privileged Noise. - Daniel Hernández-Lobato; Viktoriia Sharmanska; Kristian Kersting; Christoph H. Lampert; Novi Quadrianto (NIPS, 2014)

A Double Pruning Scheme for Boosting Ensembles. - Víctor Soto; Sergio García-Moratilla; Gonzalo Martínez-Muñoz; Daniel Hernández-Lobato; Alberto Suárez 001 (IEEE Trans. Cybern., 2014)

A Probabilistic Model for Dirty Multi-task Feature Selection. - Daniel Hernández-Lobato; José Miguel Hernández-Lobato; Zoubin Ghahramani (ICML, 2015)

Special Issue on "Solving complex machine learning problems with ensemble methods". - Daniel Hernández-Lobato; Ioannis Katakis 001; Gonzalo Martínez-Muñoz; Ioannis Partalas (Neurocomputing, 2015)

Expectation propagation in linear regression models with spike-and-slab priors. - José Miguel Hernández-Lobato; Daniel Hernández-Lobato; Alberto Suárez 001 (Mach. Learn., 2015)

Scalable Gaussian Process Classification via Expectation Propagation. - Daniel Hernández-Lobato; José Miguel Hernández-Lobato (AISTATS, 2016)

Ambiguity Helps: Classification with Disagreements in Crowdsourced Annotations. - Viktoriia Sharmanska; Daniel Hernández-Lobato; José Miguel Hernández-Lobato; Novi Quadrianto (CVPR, 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 (ICML, 2016)

Predictive Entropy Search for Multi-objective Bayesian Optimization. - Daniel Hernández-Lobato; José Miguel Hernández-Lobato; Amar Shah 001; Ryan P. Adams (ICML, 2016)

Black-Box Alpha Divergence Minimization. - José Miguel Hernández-Lobato; Yingzhen Li; Mark Rowland 001; Thang D. Bui; Daniel Hernández-Lobato; Richard E. Turner (ICML, 2016)

Non-linear Causal Inference using Gaussianity Measures. - Daniel Hernández-Lobato; Pablo Morales-Mombiela; David Lopez-Paz; Alberto Suárez 001 (J. Mach. Learn. Res., 2016)

Scalable Multi-Class Gaussian Process Classification using Expectation Propagation. - Carlos Villacampa-Calvo; Daniel Hernández-Lobato (ICML, 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 (IWANN (1), 2017)

Bayesian Optimization of the PC Algorithm for Learning Gaussian Bayesian Networks. - Irene Córdoba; Eduardo C. Garrido-Merchán; Daniel Hernández-Lobato; Concha Bielza; Pedro Larrañaga (CAEPIA, 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)

Non-linear Causal Inference Using Gaussianity Measures. - Daniel Hernández-Lobato; Pablo Morales-Mombiela; David Lopez-Paz; Alberto Suárez 001 (Cause Effect Pairs in Machine Learning, 2019)

Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints. - Eduardo C. Garrido-Merchán; Daniel Hernández-Lobato (Neurocomputing, 2019)

Importance Weighted Adversarial Variational Bayes. - Marta Gómez-Sancho; Daniel Hernández-Lobato (HAIS, 2020)

Deep Gaussian Processes Using Expectation Propagation and Monte Carlo Methods. - Gonzalo Hernández-Muñoz; Carlos Villacampa-Calvo; Daniel Hernández-Lobato (ECML/PKDD (3), 2020)

Parallel Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints. - Eduardo C. Garrido-Merchán; Daniel Hernández-Lobato (CoRR, 2020)

Max-value Entropy Search for Multi-objective Bayesian Optimization with Constraints. - Daniel Fernández-Sánchez; Eduardo C. Garrido-Merchán; Daniel Hernández-Lobato (CoRR, 2020)

Dealing with categorical and integer-valued variables in Bayesian Optimization with Gaussian processes. - Eduardo C. Garrido-Merchán; Daniel Hernández-Lobato (Neurocomputing, 2020)

Alpha divergence minimization in multi-class Gaussian process classification. - Carlos Villacampa-Calvo; Daniel Hernández-Lobato (Neurocomputing, 2020)

Activation-level uncertainty in deep neural networks. - Pablo Morales-Alvarez; Daniel Hernández-Lobato; Rafael Molina 001; José Miguel Hernández-Lobato (ICLR, 2021)

Sparse Implicit Processes for Approximate Inference. - Simón Rodríguez Santana; Bryan Zaldivar; Daniel Hernández-Lobato (CoRR, 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)

Input Dependent Sparse Gaussian Processes. - Bahram Jafrasteh; Carlos Villacampa-Calvo; Daniel Hernández-Lobato (ICML, 2022)

Function-space Inference with Sparse Implicit Processes. - Simón Rodríguez Santana; Bryan Zaldivar; Daniel Hernández-Lobato (ICML, 2022)

Correcting Model Bias with Sparse Implicit Processes. - Simón Rodríguez Santana; Luis A. Ortega Andrés; Daniel Hernández-Lobato; Bryan Zaldivar (CoRR, 2022)

Alpha-divergence minimization for deep Gaussian processes. - Carlos Villacampa-Calvo; Gonzalo Hernández-Muñoz; Daniel Hernández-Lobato (Int. J. Approx. Reason., 2022)

Adversarial α-divergence minimization for Bayesian approximate inference. - Simón Rodríguez Santana; Daniel Hernández-Lobato (Neurocomputing, 2022)

Deep Variational Implicit Processes. - Luis A. Ortega 001; Simón Rodríguez Santana; Daniel Hernández-Lobato (ICLR, 2023)

Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification. - Juan Maroñas; Daniel Hernández-Lobato (ICML, 2023)

Deep Transformed Gaussian Processes. - Francisco Javier Sáez-Maldonado; Juan Maroñas; Daniel Hernández-Lobato (CoRR, 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)

Gaussian processes for missing value imputation. - Bahram Jafrasteh; Daniel Hernández-Lobato; Simón Pedro Lubián-López; Isabel Benavente-Fernández (Knowl. Based Syst., 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., 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., 2023)

Joint Entropy Search for Multi-objective Bayesian Optimization with Constraints and Multiple Fidelities. - Daniel Fernández-Sánchez; Daniel Hernández-Lobato (ESANN, 2024)

Variational Linearized Laplace Approximation for Bayesian Deep Learning. - Luis A. Ortega Andrés; Simón Rodríguez Santana; Daniel Hernández-Lobato (ICML, 2024)

Fixed-Mean Gaussian Processes for Post-hoc Bayesian Deep Learning. - Luis A. Ortega 001; Simón Rodríguez Santana; Daniel Hernández-Lobato (CoRR, 2024)

Alpha entropy search for new information-based Bayesian optimization. - Daniel Fernández-Sánchez; Eduardo C. Garrido-Merchán; Daniel Hernández-Lobato (Knowl. Based Syst., 2025)

Robust-Multi-Task Gradient Boosting. - Seyedsaman Emami; Gonzalo Martínez-Muñoz; Daniel Hernández-Lobato (CoRR, 2025)