Aggregation of Multiple Knockoffs

Tuan-Binh Nguyen · Jerome-Alexis Chevalier · Thirion Bertrand · Sylvain Arlot

Keywords: [ Computational Biology and Genomics ] [ Neuroscience and Cognitive Science ] [ Robust Statistics and Machine Learning ] [ Supervised Learning ] [ Boosting / Ensemble Methods ]


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.

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