D. Hernández-Lobato

发表

Black-box α-divergence minimization

Richard E. Turner, José Miguel Hernández-Lobato, Mark Rowland, 2016, ICML 2016.

Prediction based on averages over automatically induced learners: ensemble methods and Bayesian techniques

D. Hernández-Lobato, 2009 .

Importance Weighted Autoencoders with Random Neural Network Parameters

José Miguel Hernández-Lobato, D. Hernández-Lobato, 2016 .

M L ] 1 0 N ov 2 01 5 Black-box α-divergence Minimization

Richard E. Turner, José Miguel Hernández-Lobato, Yingzhen Li, 2015 .

Importance Weighted Adversarial Variational Bayes

Daniel Hernández-Lobato, Marta Gómez-Sancho, D. Hernández-Lobato, 2020, HAIS.

Inference over radiative transfer models using variational and expectation maximization methods

Daniel Hernández-Lobato, Luca Martino, Valero Laparra, 2021, Machine Learning.

Variance estimators for t-Test ranking influence the stability and predictive performance of microarray gene signatures

D. Hernández-Lobato, P. Dupont, N. Touleimat, 2010, ECCB 2010.

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

Daniel Hernández-Lobato, Pierre Dupont, José Miguel Hernández-Lobato, 2013, J. Mach. Learn. Res..

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

Daniel Hernández-Lobato, Ioannis Partalas, Ioannis Katakis, 2015, Neurocomputing.

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

L. Cornejo-Bueno, S. Salcedo-Sanz, E.C. Garrido-Merchán, 2018, Neurocomputing.

Network-based sparse Bayesian classification

Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Alberto Suárez, 2011, Pattern Recognit..

Expectation Propagation for Bayesian Multi-task Feature Selection

Thibault Helleputte, Daniel Hernández-Lobato, Pierre Dupont, 2010, ECML/PKDD.

Dealing with Categorical and Integer-valued Variables in Bayesian Optimization with Gaussian Processes

Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato, D. Hernández-Lobato, 2017, Neurocomputing.

Expectation Propagation for microarray data classification

Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Alberto Suárez, 2010, Pattern Recognit. Lett..

Deep Gaussian Processes for Regression using Approximate Expectation Propagation

Daniel Hernández-Lobato, Richard E. Turner, Thang D. Bui, 2016, ICML.

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

Daniel Hernández-Lobato, Gonzalo Martínez-Muñoz, Alberto Suárez, 2011, Neurocomputing.

Pruning in Ordered Regression Bagging Ensembles

Daniel Hernández-Lobato, Gonzalo Martínez-Muñoz, Alberto Suárez, 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

Activation-level uncertainty in deep neural networks

Daniel Hernández-Lobato, Pablo Morales-Alvarez, Rafael Molina, 2021, ICLR.

Black-Box Alpha Divergence Minimization

Daniel Hernández-Lobato, Richard E. Turner, Thang D. Bui, 2015, ICML.

Heterogeneity of Synovial Molecular Patterns in Patients with Arthritis

Thibault Helleputte, Daniel Hernández-Lobato, Julie Ducreux, 2015, PloS one.

Robust Multi-Class Gaussian Process Classification

Daniel Hernández-Lobato, Pierre Dupont, José Miguel Hernández-Lobato, 2011, NIPS.

Ambiguity Helps: Classification with Disagreements in Crowdsourced Annotations

Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Novi Quadrianto, 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Learning Feature Selection Dependencies in Multi-task Learning

Daniel Hernández-Lobato, José Miguel Hernández-Lobato, D. Hernández-Lobato, 2013, NIPS.

An Analysis of Ensemble Pruning Techniques Based on Ordered Aggregation

Daniel Hernández-Lobato, Gonzalo Martínez-Muñoz, Alberto Suárez, 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Alberto Suárez, 2015, Machine Learning.

A Double Pruning Scheme for Boosting Ensembles

Daniel Hernández-Lobato, Víctor Soto, Gonzalo Martínez-Muñoz, 2014, IEEE Transactions on Cybernetics.

Adversarial α-divergence Minimization for Bayesian Approximate Inference pdf

Daniel Hernández-Lobato, Simón Rodríguez Santana, D. Hernández-Lobato, 2020, ArXiv.

Alpha divergence minimization in multi-class Gaussian process classification

Daniel Hernández-Lobato, Carlos Villacampa-Calvo, D. Hernández-Lobato, 2020, Neurocomputing.

Scalable Gaussian Process Classification via Expectation Propagation

Daniel Hernández-Lobato, José Miguel Hernández-Lobato, D. Hernández-Lobato, 2015, AISTATS.

How large should ensembles of classifiers be?

Daniel Hernández-Lobato, Gonzalo Martínez-Muñoz, Alberto Suárez, 2013, Pattern Recognit..

A Probabilistic Model for Dirty Multi-task Feature Selection

Daniel Hernández-Lobato, Zoubin Ghahramani, José Miguel Hernández-Lobato, 2015, ICML.

Scalable Multi-Class Gaussian Process Classification using Expectation Propagation

Daniel Hernández-Lobato, Carlos Villacampa-Calvo, D. Hernández-Lobato, 2017, ICML.

Alpha-divergence minimization for deep Gaussian processes

D. Hernández-Lobato, Carlos Villacampa-Calvo, Gonzalo Hernández-Muñoz, 2022, Int. J. Approx. Reason..

Bayesian optimization of the PC algorithm for learning Gaussian Bayesian networks

Irene Córdoba-Sánchez, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato, 2018, CAEPIA.

Mind the Nuisance: Gaussian Process Classification using Privileged Noise

Christoph H. Lampert, Daniel Hernández-Lobato, Kristian Kersting, 2014, NIPS.

Non-linear Causal Inference using Gaussianity Measures

Daniel Hernández-Lobato, David Lopez-Paz, Alberto Suárez, 2014, J. Mach. Learn. Res..

Statistical Instance-Based Pruning in Ensembles of Independent Classifiers

Daniel Hernández-Lobato, Gonzalo Martínez-Muñoz, Alberto Suárez, 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Supplementary Material for : A Probabilistic Model for Dirty Multitask Feature Selection

D. Hernández-Lobato, 2015 .

Black-Box Alpha Divergence Minimization

Richard E. Turner, José Miguel Hernández-Lobato, Mark Rowland, 2016, ICML.

Predictive Entropy Search for Multi-objective Bayesian Optimization

Ryan P. Adams, José Miguel Hernández-Lobato, D. Hernández-Lobato, 2015, ICML.

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

D. Hernández-Lobato, E.C. Garrido-Merchán, Daniel Fernández-Sánchez, 2023, Neurocomputing.

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

D. Hernández-Lobato, E.C. Garrido-Merchán, Daniel Fernández-Sánchez, 2022, Expert Syst. Appl..

A Double Pruning Algorithm for Classification Ensembles

Daniel Hernández-Lobato, Víctor Soto, Gonzalo Martínez-Muñoz, 2010, MCS.

Out of Bootstrap Estimation of Generalization Error Curves in Bagging Ensembles

Daniel Hernández-Lobato, Gonzalo Martínez-Muñoz, Alberto Suárez, 2007, IDEAL.