Structure Discovery in Nonparametric Regression through Compositional Kernel Search

David Duvenaud, James Lloyd, Roger Grosse, Joshua Tenenbaum, Ghahramani Zoubin
; Proceedings of the 30th International Conference on Machine Learning, PMLR 28(3):1166-1174, 2013.

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.

Cite this Paper


BibTeX
@InProceedings{pmlr-v28-duvenaud13, title = {Structure Discovery in Nonparametric Regression through Compositional Kernel Search}, author = {David Duvenaud and James Lloyd and Roger Grosse and Joshua Tenenbaum and Ghahramani Zoubin}, booktitle = {Proceedings of the 30th International Conference on Machine Learning}, pages = {1166--1174}, year = {2013}, editor = {Sanjoy Dasgupta and David McAllester}, volume = {28}, number = {3}, series = {Proceedings of Machine Learning Research}, address = {Atlanta, Georgia, USA}, month = {17--19 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v28/duvenaud13.pdf}, url = {http://proceedings.mlr.press/v28/duvenaud13.html}, 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.} }
Endnote
%0 Conference Paper %T Structure Discovery in Nonparametric Regression through Compositional Kernel Search %A David Duvenaud %A James Lloyd %A Roger Grosse %A Joshua Tenenbaum %A Ghahramani Zoubin %B Proceedings of the 30th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2013 %E Sanjoy Dasgupta %E David McAllester %F pmlr-v28-duvenaud13 %I PMLR %J Proceedings of Machine Learning Research %P 1166--1174 %U http://proceedings.mlr.press %V 28 %N 3 %W PMLR %X 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.
RIS
TY - CPAPER TI - Structure Discovery in Nonparametric Regression through Compositional Kernel Search AU - David Duvenaud AU - James Lloyd AU - Roger Grosse AU - Joshua Tenenbaum AU - Ghahramani Zoubin BT - Proceedings of the 30th International Conference on Machine Learning PY - 2013/02/13 DA - 2013/02/13 ED - Sanjoy Dasgupta ED - David McAllester ID - pmlr-v28-duvenaud13 PB - PMLR SP - 1166 DP - PMLR EP - 1174 L1 - http://proceedings.mlr.press/v28/duvenaud13.pdf UR - http://proceedings.mlr.press/v28/duvenaud13.html AB - 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. ER -
APA
Duvenaud, D., Lloyd, J., Grosse, R., Tenenbaum, J. & Zoubin, G.. (2013). Structure Discovery in Nonparametric Regression through Compositional Kernel Search. Proceedings of the 30th International Conference on Machine Learning, in PMLR 28(3):1166-1174

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