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arXiv · 1302.4922

Structure Discovery in Nonparametric Regression through Compositional Kernel Search

David Duvenaud·James Robert Lloyd·Roger Grosse·Joshua B. Tenenbaum·Zoubin Ghahramani
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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.

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David Duvenaud, James Robert Lloyd, Roger Grosse, Joshua B. Tenenbaum, Zoubin Ghahramani. 2013-02-20. Structure Discovery in Nonparametric Regression through Compositional Kernel Search. https://arxiv.org/abs/1302.4922

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