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Conditionally Independent Multiresolution Gaussian Processes
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.ORCID iD: 0000-0001-5183-234X
2019 (English)In: 22nd International Conference On Artificial Intelligence And Statistics, Vol 89 / [ed] Chaudhuri, K Sugiyama, M, 2019Conference paper, Published paper (Refereed)
Abstract [en]

The multiresolution Gaussian process (GP) has gained increasing attention as a viable approach towards improving the quality of approximations in GPs that scale well to large-scale data. Most of the current constructions assume full independence across resolutions. This assumption simplifies the inference, but it underestimates the uncertainties in transitioning from one resolution to another. This in turn results in models which are prone to overfitting in the sense of excessive sensitivity to the chosen resolution, and predictions which are non-smooth at the boundaries. Our contribution is a new construction which instead assumes conditional independence among GPs across resolutions. We show that relaxing the full independence assumption enables robustness against overfitting, and that it delivers predictions that are smooth at the boundaries. Our new model is compared against current state of the art on 2 synthetic and 9 real-world datasets. In most cases, our new conditionally independent construction performed favorably when compared against models based on the full independence assumption. In particular, it exhibits little to no signs of overfitting.

Place, publisher, year, edition, pages
2019.
Series
Proceedings of Machine Learning Research, ISSN 2640-3498 ; 89
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-407479ISI: 000509687901001OAI: oai:DiVA.org:uu-407479DiVA, id: diva2:1416845
Conference
22nd International Conference on Artificial Intelligence and Statistics (AISTATS), APR 16-18, 2019, Naha, JAPAN
Funder
Knut and Alice Wallenberg Foundation, KAW2014.0392Swedish Research Council, 621-2016-06079Available from: 2020-03-25 Created: 2020-03-25 Last updated: 2020-03-25Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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  • de-DE
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  • en-US
  • fi-FI
  • nn-NO
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  • asciidoc
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