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Sparse Approximation for Gaussian Process with Derivative Observations

  • Ang Yang
  • Cheng Li
  • Santu Rana
  • Sunil Gupta
  • Svetha Venkatesh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11320)

Abstract

We propose a sparse Gaussian process model to approximate full Gaussian process with derivatives when a large number of function observations t and derivative observations \(t'\) exist. By introducing a small number of inducing point m, the complexity of posterior computation can be reduced from \(\mathcal {O}((t+t')^{3})\) to \(\mathcal {O}((t+t')m^{2})\). We also find the usefulness of our approach in Bayesian optimisation. Experiments demonstrate the superiority of our approach.

Keywords

Sparse Gaussian process model Bayesian optimisation Derivative-based 

Notes

Acknowledgment

This research was partially funded by the Australian Government through the Australian Research Council (ARC). Prof Venkatesh is the recipient of an ARC Australian Laureate Fellowship (FL170100006).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Ang Yang
    • 1
  • Cheng Li
    • 1
  • Santu Rana
    • 1
  • Sunil Gupta
    • 1
  • Svetha Venkatesh
    • 1
  1. 1.Center for Pattern Recognition and Data AnalyticsDeakin UniversityGeelongAustralia

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