Duvenaud, D and Lloyd, JR and Grosse, R and Tenenbaum, JB and Ghahramani, Z Structure Discovery in Nonparametric Regression through Compositional Kernel Search. (Unpublished)Full text not available from this repository.
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.
|Uncontrolled Keywords:||stat.ML stat.ML cs.LG stat.ME G.3; I.2.6|
|Divisions:||Div F > Computational and Biological Learning|
|Depositing User:||Unnamed user with email firstname.lastname@example.org|
|Date Deposited:||15 Dec 2015 12:50|
|Last Modified:||09 Feb 2016 23:26|