Recent Advances in Scaling Up Gaussian Process Predictive Models for Large Spatiotemporal Data
Author(s)
Low, Kian Hsiang; Chen, Jie; Hoang, Trong Nghia; Xu, Nuo; Jaillet, Patrick
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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.
Date issued
2015-11Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer ScienceJournal
Dynamic Data-Driven Environmental Systems Science
Publisher
Springer-Verlag
Citation
Low, Kian Hsiang, Jie Chen, Trong Nghia Hoang, Nuo Xu, and Patrick Jaillet. “Recent Advances in Scaling Up Gaussian Process Predictive Models for Large Spatiotemporal Data.” Lecture Notes in Computer Science (2015): 167–181.
Version: Author's final manuscript
ISBN
978-3-319-25137-0
978-3-319-25138-7
ISSN
0302-9743
1611-3349