Aggregation of Multiple Knockoffs

We develop an extension of the knockoff inference procedure, introduced by Barber and Candès [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. †