Skip to Main content Skip to Navigation
Conference papers

Aggregation of Multiple Knockoffs

Abstract : We develop an extension of the knockoff inference procedure, introduced by Barber and Candès [2015]. This new method, called ag-gregation 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.
Complete list of metadata

Cited literature [25 references]  Display  Hide  Download
Contributor : Binh T. Nguyen Connect in order to contact the contributor
Submitted on : Friday, July 3, 2020 - 11:32:16 AM
Last modification on : Wednesday, April 20, 2022 - 3:44:07 AM
Long-term archiving on: : Thursday, September 24, 2020 - 7:31:16 AM


Files produced by the author(s)


  • HAL Id : hal-02888693, version 1


Tuan-Binh Nguyen, Jérôme-Alexis Chevalier, Bertrand Thirion, Sylvain Arlot. Aggregation of Multiple Knockoffs. ICML 2020 - 37th International Conference on Machine Learning, Jul 2020, Vienne / Virtual, Austria. ⟨hal-02888693⟩



Record views


Files downloads