Structure discovery in nonparametric regression through compositional kernel search
Name
Tenenbaum_Structure Discovery.pdf
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1.43 MB
Format
Adobe PDF
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Author(s)
Duvenaud, David
Lloyd, James Robert
Grosse, Roger Baker
Tenenbaum, Joshua B.
Ghahramani, Zoubin
Date Issued
June 2013
Journal
Proceedings of the 30th International Conference on Machine Learning
Publisher
International Machine Learning Society
Citation
Duvenaud, David, James Robert Lloyd, Roger Grosse, Joshua B. Tenenbaum, and Zoubin Ghahramani. "Structure discovery in nonparametric regression through compositional kernel search." 30th International Conference on Machine Learning (June 2013).
Version
Original manuscript
Abstract
Despite its importance, choosing the structural form of the kernel in nonparametric regression remains a black art. We define a space of kernel structures which are built compositionally by adding and multiplying a small number of base kernels. We present a method for searching over this space of structures which mirrors the scientific discovery process. The learned structures can often decompose functions into interpretable components and enable long-range extrapolation on time-series datasets. Our structure search method outperforms many widely used kernels and kernel combination methods on a variety of prediction tasks.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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