Structure Discovery in Nonparametric Regression through Compositional Kernel Search [article]

David Duvenaud, James Robert Lloyd, Roger Grosse, Joshua B. Tenenbaum, Zoubin Ghahramani
2013 arXiv   pre-print
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
more » ... Our structure search method outperforms many widely used kernels and kernel combination methods on a variety of prediction tasks.
arXiv:1302.4922v4 fatcat:qrtyoyzyajbkdnqp64sbirbfii