Probabilistic selection of inducing points in sparse gaussian processes

Uhrenholt, A. K. , Charvet, V. and Jensen, B. S. (2021) Probabilistic selection of inducing points in sparse gaussian processes. In: 37th Conference on Uncertainty in Artificial Intelligence (UAI 2021), 27-29 Jul 2021, pp. 1035-1044.

Abstract

Sparse Gaussian processes and various extensions thereof are enabled through inducing points, that simultaneously bottleneck the predictive capacity and act as the main contributor towards model complexity. However, the number of inducing points is generally not associated with uncertainty which prevents us from applying the apparatus of Bayesian reasoning for identifying an appropriate trade-off. In this work we place a point process prior on the inducing points and approximate the associated posterior through stochastic variational inference. By letting the prior encourage a moderate number of inducing points, we enable the model to learn which and how many points to utilise. We experimentally show that fewer inducing points are preferred by the model as the points become less informative, and further demonstrate how the method can be employed in deep Gaussian processes and latent variable modelling.

• Item Type: Conference Proceedings
• Additional Information: BSJ and VC acknowledge support from EPSRC grant EP/R018634/1: Closed-Loop Data Science for Complex, Computationally- and Data-Intensive Analytics.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Charvet, Valentin and Jensen, Dr Bjorn and Uhrenholt, Anders
• Authors: Uhrenholt, A. K., Charvet, V., and Jensen, B. S.
• College/School: College of Science and Engineering > School of Computing Science
• ISSN: 2640-3498
• Copyright Holders: Copyright © 2021 The Author(s)
• Publisher Policy: Reproduced under a Creative Commons License

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Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
300982		Exploiting Closed-Loop Aspects in Computationally and Data Intensive Analytics	Roderick Murray-Smith	Engineering and Physical Sciences Research Council (EPSRC)	EP/R018634/1	Computing Science