Additive Gaussian Processes Revisited

• URL: http://arxiv.org/abs/2206.09861v1
• Date: Mon, 20 Jun 2022 15:52:59 GMT
• Title: Additive Gaussian Processes Revisited
• Authors: Xiaoyu Lu, Alexis Boukouvalas, James Hensman
• Abstract summary: We propose a new class of flexible non-parametric GP models with additive structure. We show that the OAK model achieves similar or better predictive performance compared to black-box models. 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.
• Score: 13.158344774468413
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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