# Aggregation of Multiple Knockoffs

arXiv: https://arxiv.org/abs/2002.09269 · HTML: https://mlsanity.com/paper/2002.09269/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2020.

## Built on by (most cited first, 6 of 6)

- [Statistically Valid Variable Importance Assessment through Conditional Permutations](https://mlsanity.com/paper/2309.07593/) (2023)
- [Data-driven sparse identification of governing PDEs via knockoff filters and multi-criteria trade-offs](https://mlsanity.com/paper/2605.26631/) (2026)
- [Spatially relaxed inference on high-dimensional linear models](https://mlsanity.com/paper/2106.02590/) (2021)
- [Knoop: Practical Enhancement of Knockoff with Over-Parameterization for Variable Selection](https://mlsanity.com/paper/2501.17889/) (2025)
- [Using latent representations to link disjoint longitudinal data for mixed-effects regression](https://mlsanity.com/paper/2510.25531/) (2025)
- [Extending Model-x Framework to Missing Data](https://mlsanity.com/paper/2202.13054/) (2022)

## Abstract (arXiv)

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.