Scalable Variational Gaussian Process Classification

Abstract

Gaussian process classification is a popular method with a number of appealing properties. We show how to scale the model within a variational inducing point framework, outperforming the state of the art on benchmark datasets. Importantly, the variational formulation can be exploited to allow classification in problems with millions of data points, as we demonstrate in experiments.

16 pages, 9 figures

References in corpus (2)

• ADADELTA: An Adaptive Learning Rate Method
• Gaussian Process Models with Parallelization and GPU acceleration

Cited by in corpus (1)

• MCMC for Variationally Sparse Gaussian Processes