| Alpha-divergence minimization for deep Gaussian processes | 0 | 0.34 | 2022 |
| Function-space Inference with Sparse Implicit Processes. | 0 | 0.34 | 2022 |
| Input Dependent Sparse Gaussian Processes. | 0 | 0.34 | 2022 |
| Activation-level uncertainty in deep neural networks | 0 | 0.34 | 2021 |
| Multi-Class Gaussian Process Classification With Noisy Inputs | 0 | 0.34 | 2021 |
| Dealing with categorical and integer-valued variables in Bayesian Optimization with Gaussian processes | 6 | 0.46 | 2020 |
| Alpha Divergence Minimization in Multi-Class Gaussian Process Classification | 1 | 0.43 | 2020 |
| Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints | 3 | 0.42 | 2019 |
| Bayesian optimization of a hybrid system for robust ocean wave features prediction. | 0 | 0.34 | 2018 |
| Bayesian optimization of the PC algorithm for learning Gaussian Bayesian networks. | 0 | 0.34 | 2018 |
| Bayesian Optimization of a Hybrid Prediction System for Optimal Wave Energy Estimation Problems. | 0 | 0.34 | 2017 |
| Scalable Multi-Class Gaussian Process Classification using Expectation Propagation. | 0 | 0.34 | 2017 |
| Non-linear Causal Inference using Gaussianity Measures | 0 | 0.34 | 2016 |
| Scalable Gaussian Process Classification via Expectation Propagation | 0 | 0.34 | 2016 |
| Predictive Entropy Search for Multi-objective Bayesian Optimization | 11 | 0.58 | 2016 |
| Black-Box Alpha Divergence Minimization. | 0 | 0.34 | 2016 |
| Deep Gaussian Processes for Regression using Approximate Expectation Propagation. | 22 | 0.85 | 2016 |
| Ambiguity Helps: Classification With Disagreements In Crowdsourced Annotations | 0 | 0.34 | 2016 |
| Expectation propagation in linear regression models with spike-and-slab priors | 16 | 0.89 | 2015 |
| A Probabilistic Model for Dirty Multi-task Feature Selection | 7 | 0.41 | 2015 |
| Special Issue on "Solving complex machine learning problems with ensemble methods". | 0 | 0.34 | 2015 |
| A double pruning scheme for boosting ensembles. | 11 | 0.43 | 2014 |
| Mind the Nuisance: Gaussian Process Classification using Privileged Noise. | 3 | 0.40 | 2014 |
| How large should ensembles of classifiers be? | 18 | 0.64 | 2013 |
| Statistical tests for the detection of the arrow of time in vector autoregressive models | 1 | 0.40 | 2013 |
| Learning Feature Selection Dependencies in Multi-task Learning. | 8 | 0.48 | 2013 |
| Gaussian Process Conditional Copulas with Applications to Financial Time Series. | 1 | 0.36 | 2013 |
| Generalized spike-and-slab priors for Bayesian group feature selection using expectation propagation | 28 | 1.20 | 2013 |
| On the Independence of the Individual Predictions in Parallel Randomized Ensembles. | 4 | 0.47 | 2012 |
| Network-based sparse Bayesian classification | 9 | 0.71 | 2011 |
| Inference on the prediction of ensembles of infinite size | 7 | 0.61 | 2011 |
| Robust Multi-Class Gaussian Process Classification. | 9 | 0.51 | 2011 |
| Empirical analysis and evaluation of approximate techniques for pruning regression bagging ensembles | 18 | 0.73 | 2011 |
| Expectation Propagation for microarray data classification | 12 | 0.60 | 2010 |
| A double pruning algorithm for classification ensembles | 6 | 0.45 | 2010 |
| Expectation propagation for Bayesian multi-task feature selection | 12 | 0.68 | 2010 |
| Statistical Instance-Based Ensemble Pruning for Multi-class Problems | 4 | 0.41 | 2009 |
| An analysis of ensemble pruning techniques based on ordered aggregation. | 147 | 3.50 | 2009 |
| Statistical instance-based pruning in ensembles of independent classifiers. | 24 | 0.94 | 2009 |
| Class-switching neural network ensembles | 18 | 0.68 | 2008 |
| Bayes Machines for binary classification | 2 | 0.39 | 2008 |
| Sparse Bayes Machines for Binary Classification | 0 | 0.34 | 2008 |
| Selection of decision stumps in bagging ensembles | 6 | 0.49 | 2007 |
| Out of bootstrap estimation of generalization error curves in bagging ensembles | 3 | 0.38 | 2007 |
| GARCH processes with non-parametric innovations for market risk estimation | 2 | 0.40 | 2007 |
| Pruning In Ordered Regression Bagging Ensembles | 12 | 0.60 | 2006 |
| Pruning adaptive boosting ensembles by means of a genetic algorithm | 9 | 0.54 | 2006 |
