Efficient Gaussian Process Classification Using Pólya-Gamma Data Augmentation
Abstract
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.
Cite
Text
Wenzel et al. "Efficient Gaussian Process Classification Using Pólya-Gamma Data Augmentation." AAAI Conference on Artificial Intelligence, 2019. doi:10.1609/AAAI.V33I01.33015417Markdown
[Wenzel et al. "Efficient Gaussian Process Classification Using Pólya-Gamma Data Augmentation." AAAI Conference on Artificial Intelligence, 2019.](https://mlanthology.org/aaai/2019/wenzel2019aaai-efficient/) doi:10.1609/AAAI.V33I01.33015417BibTeX
@inproceedings{wenzel2019aaai-efficient,
title = {{Efficient Gaussian Process Classification Using Pólya-Gamma Data Augmentation}},
author = {Wenzel, Florian and Galy-Fajou, Théo and Donner, Christian and Kloft, Marius and Opper, Manfred},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2019},
pages = {5417-5424},
doi = {10.1609/AAAI.V33I01.33015417},
url = {https://mlanthology.org/aaai/2019/wenzel2019aaai-efficient/}
}