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arXiv · 2206.09861

Additive Gaussian Processes Revisited

Xiaoyu Lu·Alexis Boukouvalas·James Hensman
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Abstract

Gaussian Process (GP) models are a class of flexible non-parametric models that have rich representational power. By using a Gaussian process with additive structure, complex responses can be modelled whilst retaining interpretability. Previous work showed that additive Gaussian process models require high-dimensional interaction terms. We propose the orthogonal additive kernel (OAK), which imposes an orthogonality constraint on the additive functions, enabling an identifiable, low-dimensional representation of the functional relationship. We connect the OAK kernel to functional ANOVA decomposition, and show improved convergence rates for sparse computation methods. With only a small number of additive low-dimensional terms, we demonstrate the OAK model achieves similar or better predictive performance compared to black-box models, while retaining interpretability.

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Xiaoyu Lu, Alexis Boukouvalas, James Hensman. 2022-06-20. Additive Gaussian Processes Revisited. https://arxiv.org/abs/2206.09861

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