295 citations · 931 across the 14 of their papers we have counts for
14 papers
Deep Transformed Gaussian Processes
Francisco Javier Sáez-Maldonado, Juan Maroñas, Daniel Hernández-Lobato
Transformed Gaussian Processes (TGPs) are stochastic processes specified by transforming samples from the joint distribution from a prior process (typically a GP) using an invertib…
Inference over radiative transfer models using variational and expectation maximization methods
Daniel Heestermans Svendsen, Daniel Hernández-Lobato, Luca Martino +3
Earth observation from satellites offers the possibility to monitor our planet with unprecedented accuracy. Radiative transfer models (RTMs) encode the energy transfer through the…
Adversarial -divergence Minimization for Bayesian Approximate Inference
Simón Rodríguez Santana, Daniel Hernández-Lobato
Neural networks are popular state-of-the-art models for many different tasks.They are often trained via back-propagation to find a value of the weights that correctly predicts the…
Bayesian optimization of the PC algorithm for learning Gaussian Bayesian networks
Irene Córdoba, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato +2
The PC algorithm is a popular method for learning the structure of Gaussian Bayesian networks. It carries out statistical tests to determine absent edges in the network. It is henc…
Dealing with Categorical and Integer-valued Variables in Bayesian Optimization with Gaussian Processes
Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato
Bayesian Optimization (BO) methods are useful for optimizing functions that are expen- sive to evaluate, lack an analytical expression and whose evaluations can be contaminated by…
Scalable Multi-Class Gaussian Process Classification using Expectation Propagation
Carlos Villacampa-Calvo, Daniel Hernández-Lobato
This paper describes an expectation propagation (EP) method for multi-class classification with Gaussian processes that scales well to very large datasets. In such a method the est…
Dealing with Integer-valued Variables in Bayesian Optimization with Gaussian Processes
Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato
Bayesian optimization (BO) methods are useful for optimizing functions that are expensive to evaluate, lack an analytical expression and whose evaluations can be contaminated by no…
Deep Gaussian Processes for Regression using Approximate Expectation Propagation
Thang D. Bui, Daniel Hernández-Lobato, Yingzhen Li +2
Deep Gaussian processes (DGPs) are multi-layer hierarchical generalisations of Gaussian processes (GPs) and are formally equivalent to neural networks with multiple, infinitely wid…
Predictive Entropy Search for Multi-objective Bayesian Optimization
Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Amar Shah +1
We present PESMO, a Bayesian method for identifying the Pareto set of multi-objective optimization problems, when the functions are expensive to evaluate. The central idea of PESMO…
Black-box -divergence Minimization
José Miguel Hernández-Lobato, Yingzhen Li, Mark Rowland +3
Black-box alpha (BB-) is a new approximate inference method based on the minimization of -divergences. BB- scales to large datasets because it can be implemented using sto…
Scalable Gaussian Process Classification via Expectation Propagation
Daniel Hernández-Lobato, José Miguel Hernández-Lobato
Variational methods have been recently considered for scaling the training process of Gaussian process classifiers to large datasets. As an alternative, we describe here how to tra…
Non-linear Causal Inference using Gaussianity Measures
Daniel Hernández-Lobato, Pablo Morales-Mombiela, David Lopez-Paz +1
We provide theoretical and empirical evidence for a type of asymmetry between causes and effects that is present when these are related via linear models contaminated with additive…
Mind the Nuisance: Gaussian Process Classification using Privileged Noise
Daniel Hernández-Lobato, Viktoriia Sharmanska, Kristian Kersting +2
The learning with privileged information setting has recently attracted a lot of attention within the machine learning community, as it allows the integration of additional knowled…
Gaussian Process Conditional Copulas with Applications to Financial Time Series
José Miguel Hernández-Lobato, James Robert Lloyd, Daniel Hernández-Lobato
The estimation of dependencies between multiple variables is a central problem in the analysis of financial time series. A common approach is to express these dependencies in terms…