| 2026 | ESANN | Improving the Linearized Laplace Approximation via Quadratic Approximations. | Pedro Jimnez Garca-Ligero, Luis A. Ortega, Pablo Morales-lvarez, Daniel Hernndez-Lobato |
| 2026 | ESANN | Scalable Linearized Laplace Approximation via Surrogate Neural Kernel. | Luis A. Ortega, Simn Rodrguez Santana, Daniel Hernndez-Lobato |
| 2024 | ESANN | Joint Entropy Search for Multi-objective Bayesian Optimization with Constraints and Multiple Fidelities. | Daniel Fernndez-Snchez, Daniel Hernndez-Lobato |
| 2024 | ICML | Variational Linearized Laplace Approximation for Bayesian Deep Learning. | Luis A. Ortega Andrs, Simn Rodrguez Santana, Daniel Hernndez-Lobato |
| 2023 | ICLR | Deep Variational Implicit Processes. | Luis A. Ortega, Simn Rodrguez Santana, Daniel Hernndez-Lobato |
| 2023 | ICML | Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification. | Juan Maroas, Daniel Hernndez-Lobato |
| 2022 | ICML | Input Dependent Sparse Gaussian Processes. | Bahram Jafrasteh, Carlos Villacampa-Calvo, Daniel Hernndez-Lobato |
| 2022 | ICML | Function-space Inference with Sparse Implicit Processes. | Simn Rodrguez Santana, Bryan Zaldivar, Daniel Hernndez-Lobato |
| 2021 | ICLR | Activation-level uncertainty in deep neural networks. | Pablo Morales-Alvarez, Daniel Hernndez-Lobato, Rafael Molina, Jos Miguel Hernndez-Lobato |
| 2020 | HAIS | Importance Weighted Adversarial Variational Bayes. | Marta Gmez-Sancho, Daniel Hernndez-Lobato |
| 2017 | ICML | Scalable Multi-Class Gaussian Process Classification using Expectation Propagation. | Carlos Villacampa-Calvo, Daniel Hernndez-Lobato |
| 2017 | IWANN | Bayesian Optimization of a Hybrid Prediction System for Optimal Wave Energy Estimation Problems. | Laura Cornejo-Bueno, Eduardo C. Garrido-Merchn, Daniel Hernndez-Lobato, Sancho Salcedo-Sanz |
| 2016 | AISTATS | Scalable Gaussian Process Classification via Expectation Propagation. | Daniel Hernndez-Lobato, Jos Miguel Hernndez-Lobato |
| 2016 | CVPR | Ambiguity Helps: Classification with Disagreements in Crowdsourced Annotations. | Viktoriia Sharmanska, Daniel Hernndez-Lobato, Jos Miguel Hernndez-Lobato, Novi Quadrianto |
| 2016 | ICML | Deep Gaussian Processes for Regression using Approximate Expectation Propagation. | Thang D. Bui, Daniel Hernndez-Lobato, Jos Miguel Hernndez-Lobato, Yingzhen Li, Richard E. Turner |
| 2016 | ICML | Predictive Entropy Search for Multi-objective Bayesian Optimization. | Daniel Hernndez-Lobato, Jos Miguel Hernndez-Lobato, Amar Shah, Ryan P. Adams |
| 2016 | ICML | Black-Box Alpha Divergence Minimization. | Jos Miguel Hernndez-Lobato, Yingzhen Li, Mark Rowland, Thang D. Bui, Daniel Hernndez-Lobato, Richard E. Turner |
| 2015 | ICML | A Probabilistic Model for Dirty Multi-task Feature Selection. | Daniel Hernndez-Lobato, Jos Miguel Hernndez-Lobato, Zoubin Ghahramani |
| 2013 | IJCAI | Statistical Tests for the Detection of the Arrow of Time in Vector Autoregressive Models. | Pablo Morales-Mombiela, Daniel Hernndez-Lobato, Alberto Surez |
| 2012 | ESANN | On the Independence of the Individual Predictions in Parallel Randomized Ensembles. | Daniel Hernndez-Lobato, Gonzalo Martnez-Muoz, Alberto Surez |
| 2009 | ICANN | Statistical Instance-Based Ensemble Pruning for Multi-class Problems. | Gonzalo Martnez-Muoz, Daniel Hernndez-Lobato, Alberto Surez |
| 2008 | ICANN | Sparse Bayes Machines for Binary Classification. | Daniel Hernndez-Lobato |
| 2007 | ICANN | GARCH Processes with Non-parametric Innovations for Market Risk Estimation. | Jos Miguel Hernndez-Lobato, Daniel Hernndez-Lobato, Alberto Surez |
| 2007 | ICANN | Selection of Decision Stumps in Bagging Ensembles. | Gonzalo Martnez-Muoz, Daniel Hernndez-Lobato, Alberto Surez |
| 2007 | IDEAL | Out of Bootstrap Estimation of Generalization Error Curves in Bagging Ensembles. | Daniel Hernndez-Lobato, Gonzalo Martnez-Muoz, Alberto Surez |
| 2006 | ICANN | Building Ensembles of Neural Networks with Class-Switching. | Gonzalo Martnez-Muoz, Aitor Snchez-Martnez, Daniel Hernndez-Lobato, Alberto Surez |
| 2006 | IJCNN | Pruning in Ordered Regression Bagging Ensembles. | Daniel Hernndez-Lobato, Gonzalo Martnez-Muoz, Alberto Surez |
| 2006 | IDEAL | Pruning Adaptive Boosting Ensembles by Means of a Genetic Algorithm. | Daniel Hernndez-Lobato, Jos Miguel Hernndez-Lobato, Rubn Ruiz-Torrubiano, ngel Valle |