Efficient Gaussian Process Classification Using Pólya-Gamma Data Augmentation - Wenzel, Florian; Galy-Fajou, Théo; Donner, Christan; Kloft, Marius; Opper, Manfred (Proceedings of the AAAI Conference on Artificial Intelligence, 2019)
We propose a scalable stochastic variational approach to GP classification building on Pólya-Gamma data augmentation and inducing points. Unlike former approaches, we obtain closed-form updates based on natural gradients that lead to efficient optimization. We evaluate the algorithm on real-world datasets containing up to 11 million data points and demonstrate that it is up to two orders of magnitude faster than the state-of-the-art while being competitive in terms of prediction performance.