TY - JOUR AU - Wenzel, Florian AU - Galy-Fajou, Théo AU - Donner, Christan AU - Kloft, Marius AU - Opper, Manfred PY - 2019/07/17 Y2 - 2024/03/28 TI - Efficient Gaussian Process Classification Using Pólya-Gamma Data Augmentation JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 33 IS - 01 SE - AAAI Technical Track: Machine Learning DO - 10.1609/aaai.v33i01.33015417 UR - https://ojs.aaai.org/index.php/AAAI/article/view/4481 SP - 5417-5424 AB - <p>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.</p> ER -