7 citations · 8 across the 2 of their papers we have counts for
3 papers
Input Dependent Sparse Gaussian Processes
Bahram Jafrasteh, Carlos Villacampa-Calvo, Daniel Hernández-Lobato
Gaussian Processes (GPs) are Bayesian models that provide uncertainty estimates associated to the predictions made. They are also very flexible due to their non-parametric nature.…
Multi-class Gaussian Process Classification with Noisy Inputs
Carlos Villacampa-Calvo, Bryan Zaldivar, Eduardo C. Garrido-Merchán +1
It is a common practice in the machine learning community to assume that the observed data are noise-free in the input attributes. Nevertheless, scenarios with input noise are comm…
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…