# Error-based Knockoffs Inference for Controlled Feature Selection

arXiv: https://arxiv.org/abs/2203.04483 · HTML: https://mlsanity.com/paper/2203.04483/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 57 references that are in the map.
- First posted on arXiv 9 Mar 2022.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Panning for Gold: Model-X Knockoffs for High-dimensional Controlled Variable Selection](https://mlsanity.com/paper/1610.02351/) (2016)
- [Controlling the false discovery rate via knockoffs](https://mlsanity.com/paper/1404.5609/) (2014)
- [DeepPINK: reproducible feature selection in deep neural networks](https://mlsanity.com/paper/1809.01185/) (2018)
- [Deep Knockoffs](https://mlsanity.com/paper/1811.06687/) (2018)
- [A knockoff filter for high-dimensional selective inference](https://mlsanity.com/paper/1602.03574/) (2016)
- [Robust inference with knockoffs](https://mlsanity.com/paper/1801.03896/) (2018)
- [The knockoff filter for FDR control in group-sparse and multitask regression](https://mlsanity.com/paper/1602.03589/) (2016)
- [Model-free Feature Screening and FDR Control with Knockoff Features](https://mlsanity.com/paper/1908.06597/) (2019)
- [RANK: Large-Scale Inference with Graphical Nonlinear Knockoffs](https://mlsanity.com/paper/1709.00092/) (2017)
- [IPAD: Stable Interpretable Forecasting with Knockoffs Inference](https://mlsanity.com/paper/1809.05032/) (2018)
- [Searching for consistent associations with a multi-environment knockoff filter](https://mlsanity.com/paper/2106.04118/) (2021)
- [Phase Transition and Regularized Bootstrap in Large Scale t-tests with False Discovery Rate Control](https://mlsanity.com/paper/1310.4371/) (2013)
- [Nonuniformity of P-values Can Occur Early in Diverging Dimensions](https://mlsanity.com/paper/1705.03604/) (2017)
- [Competition-based control of the false discovery proportion](https://mlsanity.com/paper/2011.11939/) (2020)

## Built on by (most cited first, 1 of 1)

- [Beyond False Discovery Rate: A Stepdown Group SLOPE Approach for Grouped Variable Selection](https://mlsanity.com/paper/2603.00955/) (2026)

## Abstract (arXiv)

Recently, the scheme of model-X knockoffs was proposed as a promising solution to address controlled feature selection under high-dimensional finite-sample settings. However, the procedure of model-X knockoffs depends heavily on the coefficient-based feature importance and only concerns the control of false discovery rate (FDR). To further improve its adaptivity and flexibility, in this paper, we propose an error-based knockoff inference method by integrating the knockoff features, the error-based feature importance statistics, and the stepdown procedure together. The proposed inference procedure does not require specifying a regression model and can handle feature selection with theoretical guarantees on controlling false discovery proportion (FDP), FDR, or k-familywise error rate (k-FWER). Empirical evaluations demonstrate the competitive performance of our approach on both simulated and real data.