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Robustness of OLS to sample removals: Theoretical analysis and implications

Eyar Azar · Michael J. Feldman · Boaz Nadler首次提交 2025-12-28 · 修订 2026-08-07 · v2arXiv预印本

OLS 对样本删除的稳健性：理论分析与启示AI 解读

For learned models to be trustworthy, it is essential to verify their robustness to perturbations in the training data. Classical approaches involve uncertainty quantification via confidence intervals and bootstrap methods. In contrast, recent work proposes a more stringent form of robustness: stability to the removal of any subset of $k$ samples from the training set. In this paper, we present a theoretical study of this criterion for ordinary least squares (OLS). Our…For learned models to be trustworthy, it is essential to verify their robustness to perturbations in the training data. Classical approaches involve uncertainty quantification via confidence intervals and bootstrap methods. In contrast, recent work proposes a more stringent form of robustness: stability to the removal of any subset of $k$ samples from the training set. In this paper, we present a theoretical study of this criterion for ordinary least squares (OLS). Our…

Bootstrap 与重抽样

关联记录被引 0 次 ↗OpenAlex · 核验 2026-10-01

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