# DeepPINK: reproducible feature selection in deep neural networks

arXiv: https://arxiv.org/abs/1809.01185 · HTML: https://mlsanity.com/paper/1809.01185/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2018.

## Built on by (most cited first, 30 of 41)

- [A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning](https://mlsanity.com/paper/2311.05877/) (2023)
- [Feature Selection as Deep Sequential Generative Learning](https://mlsanity.com/paper/2403.03838/) (2024)
- [How to Find a Good Explanation for Clustering?](https://mlsanity.com/paper/2112.06580/) (2021)
- [Model-X Sequential Testing for Conditional Independence via Testing by Betting](https://mlsanity.com/paper/2210.00354/) (2022)
- [Sequential Attention for Feature Selection](https://mlsanity.com/paper/2209.14881/) (2022)
- [Supervised Feature Selection with Neuron Evolution in Sparse Neural Networks](https://mlsanity.com/paper/2303.07200/) (2023)
- [Graph Convolutional Network-based Feature Selection for High-dimensional and Low-sample Size Data](https://mlsanity.com/paper/2211.14144/) (2022)
- [A User-Guided Bayesian Framework for Ensemble Feature Selection in Life Science Applications (UBayFS)](https://mlsanity.com/paper/2104.14787/) (2021)
- [Deep neural networks with controlled variable selection for the identification of putative causal genetic variants](https://mlsanity.com/paper/2109.14719/) (2021)
- [Towards a Unified Framework for Uncertainty-aware Nonlinear Variable Selection with Theoretical Guarantees](https://mlsanity.com/paper/2204.07293/) (2022)
- [Ensembling improves stability and power of feature selection for deep learning models](https://mlsanity.com/paper/2210.00604/) (2022)
- [Unveiling the Power of Sparse Neural Networks for Feature Selection](https://mlsanity.com/paper/2408.04583/) (2024)
- [Learning to Increase the Power of Conditional Randomization Tests](https://mlsanity.com/paper/2207.01022/) (2022)
- [How good Neural Networks interpretation methods really are? A quantitative benchmark](https://mlsanity.com/paper/2304.02383/) (2023)
- [Effective Neural Network L0 Regularization With BinMask](https://mlsanity.com/paper/2304.11237/) (2023)
- [Enabling tabular deep learning when d gg n with an auxiliary knowledge graph](https://mlsanity.com/paper/2306.04766/) (2023)
- [eXplainable Artificial Intelligence (XAI) in aging clock models](https://mlsanity.com/paper/2307.13704/) (2023)
- [DeepLINK-T: deep learning inference for time series data using knockoffs and LSTM](https://mlsanity.com/paper/2404.04317/) (2024)
- [Which Sparse Autoencoder Features Are Real? Model-X Knockoffs for False Discovery Rate Control](https://mlsanity.com/paper/2511.11711/) (2025)
- [Differentially Private Variable Selection via the Knockoff Filter](https://mlsanity.com/paper/2109.05402/) (2021)
- [Error-based Knockoffs Inference for Controlled Feature Selection](https://mlsanity.com/paper/2203.04483/) (2022)
- [Deep Feature Selection Using a Novel Complementary Feature Mask](https://mlsanity.com/paper/2209.12282/) (2022)
- [DeepROCK: Error-controlled interaction detection in deep neural networks](https://mlsanity.com/paper/2309.15319/) (2023)
- [DeepDRK: Deep Dependency Regularized Knockoff for Feature Selection](https://mlsanity.com/paper/2402.17176/) (2024)
- [Nonparametric IPSS: Fast, flexible feature selection with false discovery control](https://mlsanity.com/paper/2410.02208/) (2024)
- [Comprehend, Divide, and Conquer: Feature Subspace Exploration via Multi-Agent Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2504.17356/) (2025)
- [Provable FDR Control for Deep Feature Selection: Deep MLPs and Beyond](https://mlsanity.com/paper/2512.04696/) (2025)
- [Variable Selection with the Knockoffs: Composite Null Hypotheses](https://mlsanity.com/paper/2203.02849/) (2022)
- [Error Controlled Feature Selection for Ultrahigh Dimensional and Highly Correlated Feature Space Using Deep Learning](https://mlsanity.com/paper/2209.07011/) (2022)
- [The Berkelmans-Pries Feature Importance Method: A Generic Measure of Informativeness of Features](https://mlsanity.com/paper/2301.04740/) (2023)

## Abstract (arXiv)

Deep learning has become increasingly popular in both supervised and unsupervised machine learning thanks to its outstanding empirical performance. However, because of their intrinsic complexity, most deep learning methods are largely treated as black box tools with little interpretability. Even though recent attempts have been made to facilitate the interpretability of deep neural networks (DNNs), existing methods are susceptible to noise and lack of robustness. Therefore, scientists are justifiably cautious about the reproducibility of the discoveries, which is often related to the interpretability of the underlying statistical models. In this paper, we describe a method to increase the interpretability and reproducibility of DNNs by incorporating the idea of feature selection with controlled error rate. By designing a new DNN architecture and integrating it with the recently proposed knockoffs framework, we perform feature selection with a controlled error rate, while maintaining high power. This new method, DeepPINK (Deep feature selection using Paired-Input Nonlinear Knockoffs), is applied to both simulated and real data sets to demonstrate its empirical utility.