Aggregation of Multiple Knockoffs
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.
Cite
Text
Nguyen et al. "Aggregation of Multiple Knockoffs." International Conference on Machine Learning, 2020.Markdown
[Nguyen et al. "Aggregation of Multiple Knockoffs." International Conference on Machine Learning, 2020.](https://mlanthology.org/icml/2020/nguyen2020icml-aggregation/)BibTeX
@inproceedings{nguyen2020icml-aggregation,
title = {{Aggregation of Multiple Knockoffs}},
author = {Nguyen, Tuan-Binh and Chevalier, Jerome-Alexis and Thirion, Bertrand and Arlot, Sylvain},
booktitle = {International Conference on Machine Learning},
year = {2020},
pages = {7283-7293},
volume = {119},
url = {https://mlanthology.org/icml/2020/nguyen2020icml-aggregation/}
}