Hernandez-Lobato, Daniel

26 publications

NeurIPSW 2024 Mode Collapse in Variational Deep Gaussian Processes Francisco Javier Sáez-Maldonado, Juan Maroñas, Daniel Hernández-Lobato

ICML 2024 Variational Linearized Laplace Approximation for Bayesian Deep Learning Luis A. Ortega, Simon Rodriguez Santana, Daniel Hernández-Lobato

ICLR 2023 Deep Variational Implicit Processes Luis A. Ortega, Simon Rodriguez Santana, Daniel Hernández-Lobato

ICML 2023 Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-Class Classification Juan Maroñas, Daniel Hernández-Lobato

MLJ 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

ICML 2022 Function-Space Inference with Sparse Implicit Processes Simon Rodrı́guez-Santana, Bryan Zaldivar, Daniel Hernandez-Lobato

ICML 2022 Input Dependent Sparse Gaussian Processes Bahram Jafrasteh, Carlos Villacampa-Calvo, Daniel Hernandez-Lobato

ICLR 2021 Activation-Level Uncertainty in Deep Neural Networks Pablo Morales-Alvarez, Daniel Hernández-Lobato, Rafael Molina, José Miguel Hernández-Lobato

JMLR 2021 Multi-Class Gaussian Process Classification with Noisy Inputs Carlos Villacampa-Calvo, Bryan Zaldívar, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato

ECML-PKDD 2020 Deep Gaussian Processes Using Expectation Propagation and Monte Carlo Methods Gonzalo Hernández-Muñoz, Carlos Villacampa-Calvo, Daniel Hernández-Lobato

ICML 2017 Scalable Multi-Class Gaussian Process Classification Using Expectation Propagation Carlos Villacampa-Calvo, Daniel Hernández-Lobato

CVPR 2016 Ambiguity Helps: Classification with Disagreements in Crowdsourced Annotations Viktoriia Sharmanska, Daniel Hernandez-Lobato, Jose Miguel Hernandez-Lobato, Novi Quadrianto

ICML 2016 Black-Box Alpha Divergence Minimization Jose Hernandez-Lobato, Yingzhen Li, Mark Rowland, Thang Bui, Daniel Hernandez-Lobato, Richard Turner

ICML 2016 Deep Gaussian Processes for Regression Using Approximate Expectation Propagation Thang Bui, Daniel Hernandez-Lobato, Jose Hernandez-Lobato, Yingzhen Li, Richard Turner

JMLR 2016 Non-Linear Causal Inference Using Gaussianity Measures Daniel Hernández-Lobato, Pablo Morales-Mombiela, David Lopez-Paz, Alberto Suárez

ICML 2016 Predictive Entropy Search for Multi-Objective Bayesian Optimization Daniel Hernandez-Lobato, Jose Hernandez-Lobato, Amar Shah, Ryan Adams

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

ICML 2015 A Probabilistic Model for Dirty Multi-Task Feature Selection Daniel Hernandez-Lobato, Jose Miguel Hernandez-Lobato, Zoubin Ghahramani

MLJ 2015 Expectation Propagation in Linear Regression Models with Spike-and-Slab Priors José Miguel Hernández-Lobato, Daniel Hernández-Lobato, Alberto Suárez

NeurIPS 2014 Mind the Nuisance: Gaussian Process Classification Using Privileged Noise Daniel Hernández-lobato, Viktoriia Sharmanska, Kristian Kersting, Christoph H. Lampert, Novi Quadrianto

NeurIPS 2013 Gaussian Process Conditional Copulas with Applications to Financial Time Series José Miguel Hernández-Lobato, James R Lloyd, Daniel Hernández-Lobato

JMLR 2013 Generalized Spike-and-Slab Priors for Bayesian Group Feature Selection Using Expectation Propagation Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Pierre Dupont

NeurIPS 2013 Learning Feature Selection Dependencies in Multi-Task Learning Daniel Hernández-Lobato, José Miguel Hernández-Lobato

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

NeurIPS 2011 Robust Multi-Class Gaussian Process Classification Daniel Hernández-lobato, Jose M. Hernández-lobato, Pierre Dupont

ECML-PKDD 2010 Expectation Propagation for Bayesian Multi-Task Feature Selection Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Thibault Helleputte, Pierre Dupont