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Structure Discovery in Nonparametric Regression through Compositional Kernel Search

Duvenaud, D and Lloyd, JR and Grosse, R and Tenenbaum, JB and Ghahramani, Z (2013) Structure Discovery in Nonparametric Regression through Compositional Kernel Search.

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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.

Item Type: Article
Uncontrolled Keywords: stat.ML stat.ML cs.LG stat.ME G.3; I.2.6
Subjects: UNSPECIFIED
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 07 Mar 2014 11:59
Last Modified: 01 Sep 2014 12:46
DOI:

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