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Recent Advances in Scaling Up Gaussian Process Predictive Models for Large Spatiotemporal Data

  • Kian Hsiang LowEmail author
  • Jie Chen
  • Trong Nghia Hoang
  • Nuo Xu
  • Patrick Jaillet
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8964)

Abstract

The expressive power of Gaussian process (GP) models comes at a cost of poor scalability in the size of the data. To improve their scalability, this paper presents an overview of our recent progress in scaling up GP models for large spatiotemporally correlated data through parallelization on clusters of machines, online learning, and nonmyopic active sensing/learning.

Keywords

Gaussian Process Predictive Performance Parallel Machine Time Slice Road Segment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was supported by the Singapore-MIT Alliance for Research & Technology Subaward Agreements No. 41 and No. 52.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Kian Hsiang Low
    • 1
    Email author
  • Jie Chen
    • 2
  • Trong Nghia Hoang
    • 1
  • Nuo Xu
    • 1
  • Patrick Jaillet
    • 3
  1. 1.National University of SingaporeSingaporeSingapore
  2. 2.Singapore-MIT Alliance for Research and TechnologySingaporeSingapore
  3. 3.Massachusetts Institute of TechnologyCambridgeUSA

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