Efficient Gaussian Process Classification Using Pólya-Gamma Data Augmentation
2019 ◽
Vol 33
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pp. 5417-5424
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Keyword(s):
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.
Keyword(s):
2020 ◽
Vol 34
(01)
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pp. 19-26
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2019 ◽
Vol 33
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pp. 6375-6382
Keyword(s):
2019 ◽
Vol 33
◽
pp. 3542-3549
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