Donner, Christan
Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation
Wenzel, Florian, Galy-Fajou, Theo, Donner, Christan, Kloft, Marius, Opper, Manfred
We propose an efficient stochastic variational approach to GP classification building on Polya- Gamma data augmentation and inducing points, which is based on closed-form updates of natural gradients. We evaluate the algorithm on real-world datasets containing up to 11 million data points and demonstrate that it is up to three orders of magnitude faster than the state-of-the-art while being competitive in terms of prediction performance.