Daniel Hernández-Lobato

Orcid: 0000-0001-5845-437X

Affiliations:
  • Universidad Autónoma de Madrid, Computer Science Department, Spain


According to our database1, Daniel Hernández-Lobato authored at least 64 papers between 2006 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Variational Linearized Laplace Approximation for Bayesian Deep Learning.
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.
Expert Syst. Appl., April, 2023

Inference over radiative transfer models using variational and expectation maximization methods.
Mach. Learn., March, 2023

Gaussian processes for missing value imputation.
Knowl. Based Syst., 2023

Improved max-value entropy search for multi-objective bayesian optimization with constraints.
Neurocomputing, 2023

Deep Transformed Gaussian Processes.
CoRR, 2023

Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification.
Proceedings of the International Conference on Machine Learning, 2023

Deep Variational Implicit Processes.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
Adversarial α-divergence minimization for Bayesian approximate inference.
Neurocomputing, 2022

Alpha-divergence minimization for deep Gaussian processes.
Int. J. Approx. Reason., 2022

Correcting Model Bias with Sparse Implicit Processes.
CoRR, 2022

Function-space Inference with Sparse Implicit Processes.
Proceedings of the International Conference on Machine Learning, 2022

Input Dependent Sparse Gaussian Processes.
Proceedings of the International Conference on Machine Learning, 2022

2021
Multi-class Gaussian Process Classification with Noisy Inputs.
J. Mach. Learn. Res., 2021

Sparse Implicit Processes for Approximate Inference.
CoRR, 2021

Activation-level uncertainty in deep neural networks.
Proceedings of the 9th International Conference on Learning Representations, 2021

2020
Alpha divergence minimization in multi-class Gaussian process classification.
Neurocomputing, 2020

Dealing with categorical and integer-valued variables in Bayesian Optimization with Gaussian processes.
Neurocomputing, 2020

Max-value Entropy Search for Multi-objective Bayesian Optimization with Constraints.
CoRR, 2020

Parallel Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints.
CoRR, 2020

Deep Gaussian Processes Using Expectation Propagation and Monte Carlo Methods.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2020

Importance Weighted Adversarial Variational Bayes.
Proceedings of the Hybrid Artificial Intelligent Systems - 15th International Conference, 2020

2019
Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints.
Neurocomputing, 2019

Non-linear Causal Inference Using Gaussianity Measures.
Proceedings of the Cause Effect Pairs in Machine Learning, 2019

2018
Bayesian optimization of a hybrid system for robust ocean wave features prediction.
Neurocomputing, 2018

Bayesian Optimization of the PC Algorithm for Learning Gaussian Bayesian Networks.
Proceedings of the Advances in Artificial Intelligence, 2018

2017
Bayesian Optimization of a Hybrid Prediction System for Optimal Wave Energy Estimation Problems.
Proceedings of the Advances in Computational Intelligence, 2017

Scalable Multi-Class Gaussian Process Classification using Expectation Propagation.
Proceedings of the 34th International Conference on Machine Learning, 2017

2016
Non-linear Causal Inference using Gaussianity Measures.
J. Mach. Learn. Res., 2016

Black-Box Alpha Divergence Minimization.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Predictive Entropy Search for Multi-objective Bayesian Optimization.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Deep Gaussian Processes for Regression using Approximate Expectation Propagation.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Ambiguity Helps: Classification with Disagreements in Crowdsourced Annotations.
Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016

Scalable Gaussian Process Classification via Expectation Propagation.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015
Expectation propagation in linear regression models with spike-and-slab priors.
Mach. Learn., 2015

Special Issue on "Solving complex machine learning problems with ensemble methods".
Neurocomputing, 2015

A Probabilistic Model for Dirty Multi-task Feature Selection.
Proceedings of the 32nd International Conference on Machine Learning, 2015

2014
A Double Pruning Scheme for Boosting Ensembles.
IEEE Trans. Cybern., 2014

Mind the Nuisance: Gaussian Process Classification using Privileged Noise.
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?
Pattern Recognit., 2013

Generalized spike-and-slab priors for Bayesian group feature selection using expectation propagation.
J. Mach. Learn. Res., 2013

Gaussian Process Conditional Copulas with Applications to Financial Time Series.
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.
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.
Proceedings of the IJCAI 2013, 2013

2012
On the Independence of the Individual Predictions in Parallel Randomized Ensembles.
Proceedings of the 20th European Symposium on Artificial Neural Networks, 2012

2011
Inference on the prediction of ensembles of infinite size.
Pattern Recognit., 2011

Network-based sparse Bayesian classification.
Pattern Recognit., 2011

Empirical analysis and evaluation of approximate techniques for pruning regression bagging ensembles.
Neurocomputing, 2011

Robust Multi-Class Gaussian Process Classification.
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.
Pattern Recognit. Lett., 2010

Expectation Propagation for Bayesian Multi-task Feature Selection.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2010

A Double Pruning Algorithm for Classification Ensembles.
Proceedings of the Multiple Classifier Systems, 9th International Workshop, 2010

2009
An Analysis of Ensemble Pruning Techniques Based on Ordered Aggregation.
IEEE Trans. Pattern Anal. Mach. Intell., 2009

Statistical Instance-Based Pruning in Ensembles of Independent Classifiers.
IEEE Trans. Pattern Anal. Mach. Intell., 2009

Statistical Instance-Based Ensemble Pruning for Multi-class Problems.
Proceedings of the Artificial Neural Networks, 2009

2008
Bayes Machines for binary classification.
Pattern Recognit. Lett., 2008

Class-switching neural network ensembles.
Neurocomputing, 2008

Sparse Bayes Machines for Binary Classification.
Proceedings of the Artificial Neural Networks, 2008

2007
Out of Bootstrap Estimation of Generalization Error Curves in Bagging Ensembles.
Proceedings of the Intelligent Data Engineering and Automated Learning, 2007

Selection of Decision Stumps in Bagging Ensembles.
Proceedings of the Artificial Neural Networks, 2007

GARCH Processes with Non-parametric Innovations for Market Risk Estimation.
Proceedings of the Artificial Neural Networks, 2007

2006
Pruning in Ordered Regression Bagging Ensembles.
Proceedings of the International Joint Conference on Neural Networks, 2006

Pruning Adaptive Boosting Ensembles by Means of a Genetic Algorithm.
Proceedings of the Intelligent Data Engineering and Automated Learning, 2006

Building Ensembles of Neural Networks with Class-Switching.
Proceedings of the Artificial Neural Networks, 2006


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