Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1851-1859, 2016.
Abstract
We propose the group knockoff filter, a method for false discovery rate control in a linear regression setting where the features are grouped, and we would like to select a set of relevant groups which have a nonzero effect on the response. By considering the set of true and false discoveries at the group level, this method gains power relative to sparse regression methods. We also apply our method to the multitask regression problem where multiple response variables share similar sparsity patterns across the set of possible features. Empirically, the group knockoff filter successfully controls false discoveries at the group level in both settings, with substantially more discoveries made by leveraging the group structure.
@InProceedings{pmlr-v48-daia16,
title = {The knockoff filter for FDR control in group-sparse and multitask regression},
author = {Ran Dai and Rina Barber},
booktitle = {Proceedings of The 33rd International Conference on Machine Learning},
pages = {1851--1859},
year = {2016},
editor = {Maria Florina Balcan and Kilian Q. Weinberger},
volume = {48},
series = {Proceedings of Machine Learning Research},
address = {New York, New York, USA},
month = {20--22 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v48/daia16.pdf},
url = {http://proceedings.mlr.press/v48/daia16.html},
abstract = {We propose the group knockoff filter, a method for false discovery rate control in a linear regression setting where the features are grouped, and we would like to select a set of relevant groups which have a nonzero effect on the response. By considering the set of true and false discoveries at the group level, this method gains power relative to sparse regression methods. We also apply our method to the multitask regression problem where multiple response variables share similar sparsity patterns across the set of possible features. Empirically, the group knockoff filter successfully controls false discoveries at the group level in both settings, with substantially more discoveries made by leveraging the group structure.}
}
%0 Conference Paper
%T The knockoff filter for FDR control in group-sparse and multitask regression
%A Ran Dai
%A Rina Barber
%B Proceedings of The 33rd International Conference on Machine Learning
%C Proceedings of Machine Learning Research
%D 2016
%E Maria Florina Balcan
%E Kilian Q. Weinberger
%F pmlr-v48-daia16
%I PMLR
%J Proceedings of Machine Learning Research
%P 1851--1859
%U http://proceedings.mlr.press
%V 48
%W PMLR
%X We propose the group knockoff filter, a method for false discovery rate control in a linear regression setting where the features are grouped, and we would like to select a set of relevant groups which have a nonzero effect on the response. By considering the set of true and false discoveries at the group level, this method gains power relative to sparse regression methods. We also apply our method to the multitask regression problem where multiple response variables share similar sparsity patterns across the set of possible features. Empirically, the group knockoff filter successfully controls false discoveries at the group level in both settings, with substantially more discoveries made by leveraging the group structure.
TY - CPAPER
TI - The knockoff filter for FDR control in group-sparse and multitask regression
AU - Ran Dai
AU - Rina Barber
BT - Proceedings of The 33rd International Conference on Machine Learning
PY - 2016/06/11
DA - 2016/06/11
ED - Maria Florina Balcan
ED - Kilian Q. Weinberger
ID - pmlr-v48-daia16
PB - PMLR
SP - 1851
DP - PMLR
EP - 1859
L1 - http://proceedings.mlr.press/v48/daia16.pdf
UR - http://proceedings.mlr.press/v48/daia16.html
AB - We propose the group knockoff filter, a method for false discovery rate control in a linear regression setting where the features are grouped, and we would like to select a set of relevant groups which have a nonzero effect on the response. By considering the set of true and false discoveries at the group level, this method gains power relative to sparse regression methods. We also apply our method to the multitask regression problem where multiple response variables share similar sparsity patterns across the set of possible features. Empirically, the group knockoff filter successfully controls false discoveries at the group level in both settings, with substantially more discoveries made by leveraging the group structure.
ER -
Dai, R. & Barber, R.. (2016). The knockoff filter for FDR control in group-sparse and multitask regression. Proceedings of The 33rd International Conference on Machine Learning, in PMLR 48:1851-1859
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