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Life sciences · Preprint
arXiv · September 9, 2026
Posted before peer review. The findings may change or fail to hold.
This is a preprint presenting a theoretical framework for quantifying how ensembling strategies can provide algorithmic stability guarantees across various data perturbation types. The work develops mathematical guarantees expressed via covariance operator norms and claims to provide sharper bounds than privacy-based approaches, but remains unvalidated empirically and unpublished.
Preprint.
Main theoretical result provides stability guarantees for ensembled algorithms in terms of a covariance operator norm describing the ensembling process Framework yields sharper guarantees than those obtained from privacy considerations
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Theoretical framework for algorithmic stability via ensembling with mathematical guarantees, but unpublished and without empirical validation or clinical application.
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Algorithmic stability refers to the property of an algorithm being insensitive to perturbations of the input data, where the type of perturbation may vary depending on the setting. In this work, we develop a general framework to quantify the extent to which any ensembling strategy defined via averaging can yield stability guarantees for any type of data perturbation. Our main theoretical result is a guarantee on the stability of this ensembled algorithm, given in terms of the norm of a certain covariance operator that describes the ensembling process. We show how our general framework yields interpretable and intuitive insights in several examples of perturbations of practical interest, and provides much sharper guarantees than those obtained from privacy considerations.
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