Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation

By Florian Wenzel, Théo Galy-Fajou, Christian Donner, Marius Kloft, Manfred Opper. 2018

40 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

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In Artificial Intelligence

1. Jianfei Cai
2. Yew-Soon Ong
3. Haitao Liu
4. Xiaobo Shen
5. Ethan Fetaya
6. Gal Chechik
7. Florian Wenzel
8. Aviv Navon

Citations in the corpus

• GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning (2021, methodology): “13 and the update rules of the variational parameters were adapted from (Wenzel et al., 2019) and are presented in supplementary Section A.”
• When Gaussian Process Meets Big Data: A Review of Scalable GPs (2018, methodology): “Particularly, when taking the logit/softmax inverse link function, the P`olya-Gamma data augmentation [239] offers analytical inference and posterior for GPC [237], [240].”
• Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation (2019, methodology): “Wenzel et al. (2019) propose an augmentation approach for binary GP classification leading to a conditionally conjugate model, but are limited to the binary classification setting.”