# Consistent feature selection for neural networks via Adaptive Group Lasso

arXiv: https://arxiv.org/abs/2006.00334 · HTML: https://mlsanity.com/paper/2006.00334/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2020.

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

- [Sparse-Input Neural Network using Group Concave Regularization](https://mlsanity.com/paper/2307.00344/) (2023)
- [Adaptive Group Lasso Neural Network Models for Functions of Few Variables and Time-Dependent Data](https://mlsanity.com/paper/2108.10825/) (2021)
- [Searching for Minimal Optimal Neural Networks](https://mlsanity.com/paper/2109.13061/) (2021)
- [Generative adversarial learning with optimal input dimension and its adaptive generator architecture](https://mlsanity.com/paper/2405.03723/) (2024)
- [Sparse deep neural networks for nonparametric estimation in high-dimensional sparse regression](https://mlsanity.com/paper/2406.18137/) (2024)
- [Reducing information dependency does not cause training data privacy. Adversarially non-robust features do](https://mlsanity.com/paper/2607.12354/) (2026)

## Abstract (arXiv)

One main obstacle for the wide use of deep learning in medical and engineering sciences is its interpretability. While neural network models are strong tools for making predictions, they often provide little information about which features play significant roles in influencing the prediction accuracy. To overcome this issue, many regularization procedures for learning with neural networks have been proposed for dropping non-significant features. Unfortunately, the lack of theoretical results casts doubt on the applicability of such pipelines. In this work, we propose and establish a theoretical guarantee for the use of the adaptive group lasso for selecting important features of neural networks. Specifically, we show that our feature selection method is consistent for single-output feed-forward neural networks with one hidden layer and hyperbolic tangent activation function. We demonstrate its applicability using both simulation and data analysis.