Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation


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 realworld 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

arXiv preprint

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