# Improving reproducibility by controlling random seed stability in machine learning based estimation via bagging

arXiv: https://arxiv.org/abs/2604.17694 · HTML: https://mlsanity.com/paper/2604.17694/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 20 references that are in the map.
- First posted on arXiv 20 Apr 2026.

## Builds on (most cited first, 1 of 1)

- [Bagging Provides Assumption-free Stability](https://mlsanity.com/paper/2301.12600/) (2023)

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

- [Domain-incremental audio classification using domain-specific experts and prototype classifier](https://mlsanity.com/paper/2606.22952/) (2026)

## Abstract (arXiv)

Predictions from machine learning algorithms can vary across random seeds, inducing instability in downstream debiased machine learning estimators. We formalize random seed stability via a concentration condition and prove that subbagging guarantees stability for any bounded-outcome regression algorithm. We introduce a new cross-fitting procedure, adaptive cross-bagging, which simultaneously eliminates seed dependence from both nuisance estimation and sample splitting in debiased machine learning. Numerical experiments confirm that the method achieves the targeted level of stability whereas alternatives do not. Our method incurs a small computational penalty relative to standard practice whereas alternative methods incur large penalties.