解决小样本下局部置信预测集不稳定问题，提升可靠性。
Stable Localized Conformal Prediction via Transduction
- 用源任务标签和目标无标签数据做迁移学习，稳定预测集大小。
- 在有限校准数据下，预测集方差显著降低，覆盖性仍达标。
- 适合资源受限、需高稳定性的实际部署场景。
现有置信预测评估指标（如预测效率、测试条件覆盖率）均基于校准数据的期望定义。但在实际中，当仅有一个规模有限的校准集时，尤其是局部化方法，预测集大小常表现出高度波动。本文将此问题形式化为集合稳定性，定义为给定校准数据时预测集大小条件期望的方差。为在不需额外目标任务标签的情况下提升稳定性，提出稳定置信预测（StCP），一种利用标注源任务数据与未标注目标数据的迁移学习方法。理论上，刻画了StCP的边际覆盖率与稳定性；实证表明，相较于标准置信预测方法，尤其在局部化方法中，当校准数据有限时，其预测集更加稳定。
原文摘要 · Abstract (English)
Existing evaluations of conformal prediction, such as prediction efficiency and test-conditional coverage, are defined in expectation over the calibration data. In practice, when only one calibration set of limited size is available, prediction sets often exhibit high variability in size, especially for methods with localization. We formalize this concern as set stability, defined as the variance of the conditional expectation of the set size given the calibration data. To improve stability without requiring additional target-task labels, we propose Stable Conformal Prediction (StCP), a transfer learning approach that utilizes labeled source-task data and unlabeled target data. Theoretically, we characterize the marginal coverage and stability of StCP; empirically, it delivers more stable prediction sets than standard conformal prediction methods, especially for those with localization, when calibration data are limited.
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