Structure discovery in nonparametric regression through compositional kernel search
Author(s)Duvenaud, David; Lloyd, James Robert; Grosse, Roger Baker; Tenenbaum, Joshua B.; Ghahramani, Zoubin
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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.
DepartmentMassachusetts Institute of Technology. Department of Brain and Cognitive Sciences; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Proceedings of the 30th International Conference on Machine Learning
International Machine Learning Society
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).