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.
@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.}
}
%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.
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 -
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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