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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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https://hal.archives-ouvertes.fr/hal-02888693
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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

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  • HAL Id : hal-02888693, version 1

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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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