Recent Advances in Scaling Up Gaussian Process Predictive Models for Large Spatiotemporal Data
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Jaillet_Recent Advances in Scaling up Gaussian Process.pdf
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Author(s)
Low, Kian Hsiang
Chen, Jie
Hoang, Trong Nghia
Xu, Nuo
Jaillet, Patrick
Date Issued
November 2015
Journal
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
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1007/978-3-319-25138-7_16
