2 research outputs found
Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation
We propose a scalable stochastic variational approach to GP classification
building on Polya-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