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Aggregation of Multiple Knockoffs
Tuan-Binh Nguyen · Jerome-Alexis Chevalier · Thirion Bertrand · Sylvain Arlot

Thu Jul 16 12:00 PM -- 12:45 PM & Fri Jul 17 01:00 AM -- 01:45 AM (PDT) @

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.

Author Information

Tuan-Binh Nguyen (INRIA Saclay Ile-de-France)
Jerome-Alexis Chevalier (INRIA Saclay Ile-de-France)
Thirion Bertrand (inria)
Sylvain Arlot (University Paris Sud)

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