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Conference Papers Year : 2020

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.
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Dates and versions

hal-02888693 , version 1 (03-07-2020)

Identifiers

  • HAL Id : hal-02888693 , version 1

Cite

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⟩
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