ICML 2020poster32 citations
Aggregation of Multiple Knockoffs
Tuan-Binh Nguyen, Jerome-Alexis Chevalier, Bertrand Thirion, Sylvain Arlot
Abstract
We develop an extension of the knockoff inference procedure, introduced by Barber & Candes (2015). This new method, called Aggregation of Multiple Knockoffs (AKO), addresses the instability inherent to the random nature of knockoff-based inference. Specifically, AKO improves both the stability and power compared with the original knockoff algorithm while still maintaining guarantees for false discovery rate control. We provide a new inference procedure, prove its core properties, and demonstrate its benefits in a set of experiments on synthetic and real datasets.
BibTeX
@InProceedings{pmlr-v119-nguyen20a,
title = {Aggregation of Multiple Knockoffs},
author = {Nguyen, Tuan-Binh and Chevalier, Jerome-Alexis and Thirion, Bertrand and Arlot, Sylvain},
booktitle = {Proceedings of the 37th International Conference on Machine Learning},
pages = {7283--7293},
year = {2020},
editor = {III, Hal Daumé and Singh, Aarti},
volume = {119},
series = {Proceedings of Machine Learning Research},
month = {13--18 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v119/nguyen20a/nguyen20a.pdf},
url = {https://proceedings.mlr.press/v119/nguyen20a.html},
abstract = {We develop an extension of the knockoff inference procedure, introduced by Barber & Candes (2015). This new method, called Aggregation of Multiple Knockoffs (AKO), addresses the instability inherent to the random nature of knockoff-based inference. Specifically, AKO improves both the stability and power compared with the original knockoff algorithm while still maintaining guarantees for false discovery rate control. We provide a new inference procedure, prove its core properties, and demonstrate its benefits in a set of experiments on synthetic and real datasets.}
}