  We develop an extension of the Knockoff Inference procedure, introduced by
Barber and 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.
