7 citations · 21 across the 4 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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…