Recent Advances in Scaling Up Gaussian Process Predictive Models for Large Spatiotemporal Data
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 cluster...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Springer-Verlag
2018
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| Online Access: | http://hdl.handle.net/1721.1/115059 https://orcid.org/0000-0002-8585-6566 |
| _version_ | 1826208349367042048 |
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| author | Low, Kian Hsiang Chen, Jie Hoang, Trong Nghia Xu, Nuo Jaillet, Patrick |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Low, Kian Hsiang Chen, Jie Hoang, Trong Nghia Xu, Nuo Jaillet, Patrick |
| author_sort | Low, Kian Hsiang |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T14:04:02Z |
| format | Article |
| id | mit-1721.1/115059 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T14:04:02Z |
| publishDate | 2018 |
| publisher | Springer-Verlag |
| record_format | dspace |
| spelling | mit-1721.1/1150592022-09-28T18:10:25Z Recent Advances in Scaling Up Gaussian Process Predictive Models for Large Spatiotemporal Data Low, Kian Hsiang Chen, Jie Hoang, Trong Nghia Xu, Nuo Jaillet, Patrick Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Jaillet, Patrick 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. Singapore-MIT Alliance (Subaward Agreement No. 41) Singapore-MIT Alliance (Subaward Agreement No. 52) 2018-04-27T19:12:47Z 2018-04-27T19:12:47Z 2015-11 Article http://purl.org/eprint/type/ConferencePaper 978-3-319-25137-0 978-3-319-25138-7 0302-9743 1611-3349 http://hdl.handle.net/1721.1/115059 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. https://orcid.org/0000-0002-8585-6566 en_US http://dx.doi.org/10.1007/978-3-319-25138-7_16 Dynamic Data-Driven Environmental Systems Science Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Springer-Verlag Other univ. web domain |
| spellingShingle | Low, Kian Hsiang Chen, Jie Hoang, Trong Nghia Xu, Nuo Jaillet, Patrick Recent Advances in Scaling Up Gaussian Process Predictive Models for Large Spatiotemporal Data |
| title | Recent Advances in Scaling Up Gaussian Process Predictive Models for Large Spatiotemporal Data |
| title_full | Recent Advances in Scaling Up Gaussian Process Predictive Models for Large Spatiotemporal Data |
| title_fullStr | Recent Advances in Scaling Up Gaussian Process Predictive Models for Large Spatiotemporal Data |
| title_full_unstemmed | Recent Advances in Scaling Up Gaussian Process Predictive Models for Large Spatiotemporal Data |
| title_short | Recent Advances in Scaling Up Gaussian Process Predictive Models for Large Spatiotemporal Data |
| title_sort | recent advances in scaling up gaussian process predictive models for large spatiotemporal data |
| url | http://hdl.handle.net/1721.1/115059 https://orcid.org/0000-0002-8585-6566 |
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