研究人员推出了一种新的框架——数据增强自举法（DAB），旨在统一置信区间的构建。该方法利用数据的近似不变变换，将共形预测和经典自举法等现有技术包含在内作为特例。DAB 提供理论覆盖保证，该保证根据不变性的强度进行调整，无需群结构，并将数据增强整合到统计方法中。 AI
影响 引入了一个统一的置信区间统计框架，有可能提高机器学习模型评估的可靠性。
排序理由 该集群包含一篇详细介绍新统计方法的学术论文。
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We propose the data augmented bootstrap (DAB), a framework for constructing confidence intervals from approximately invariant transformations of the data. As special cases, DAB recovers popular methods that rely on exact group symmetries, such as conformal prediction, wild bootst…
arXiv:2606.09049v1 Announce Type: cross Abstract: We propose the data augmented bootstrap (DAB), a framework for constructing confidence intervals from approximately invariant transformations of the data. As special cases, DAB recovers popular methods that rely on exact group sym…
We propose the data augmented bootstrap (DAB), a framework for constructing confidence intervals from approximately invariant transformations of the data. As special cases, DAB recovers popular methods that rely on exact group symmetries, such as conformal prediction, wild bootst…