Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation
This addresses the scalability problem for practitioners using Gaussian Process classification on large datasets.
The paper tackles the computational challenge of Gaussian Process classification by proposing a scalable stochastic variational method using Polya-Gamma data augmentation and inducing points, achieving up to 100x faster training than state-of-the-art methods while maintaining competitive prediction accuracy on datasets with up to 11 million points.
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.