# Integrated path stability selection

arXiv: https://arxiv.org/abs/2403.15877 · HTML: https://mlsanity.com/paper/2403.15877/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 58 references that are in the map.
- First posted on arXiv 23 Mar 2024.

## Builds on (most cited first, 5 of 5)

- [Confidence Intervals and Hypothesis Testing for High-Dimensional Regression](https://mlsanity.com/paper/1306.3171/) (2013)
- [Beyond L1: Faster and Better Sparse Models with skglm](https://mlsanity.com/paper/2204.07826/) (2022)
- [Controlling false discoveries in high-dimensional situations: Boosting with stability selection](https://mlsanity.com/paper/1411.1285/) (2014)
- [High-dimensional regression in practice: an empirical study of finite-sample prediction, variable selection and ranking](https://mlsanity.com/paper/1808.00723/) (2018)
- [Loss-guided Stability Selection](https://mlsanity.com/paper/2202.04956/) (2022)

## Built on by (most cited first, 2 of 2)

- [Nonparametric IPSS: Fast, flexible feature selection with false discovery control](https://mlsanity.com/paper/2410.02208/) (2024)
- [Just add noise: Debiasing tree-based variable importance in mixed data](https://mlsanity.com/paper/2609.14083/) (2026)

## Abstract (arXiv)

Stability selection is a popular method for improving feature selection algorithms. One of its key attributes is that it provides theoretical upper bounds on the expected number of false positives, E(FP), enabling false positive control in practice. However, stability selection often selects few features because existing bounds on E(FP) are relatively loose. In this paper, we introduce a novel approach to stability selection based on integrating stability paths rather than maximizing over them. This yields upper bounds on E(FP) that are much stronger than previous bounds, leading to significantly more true positives in practice for the same target E(FP). Furthermore, our method requires no more computation than the original stability selection algorithm. We demonstrate the method on simulations and real data from two cancer studies.