小样本下提升预测集覆盖率的可靠性，确保实际效果不偏离目标。
Probabilistic Conformal Coverage Guarantees in Small-Data Settings
- 引入小样本贝塔校正，基于覆盖率的有限样本分布调整显著性水平。
- 在单次校准集上实现用户指定概率下的最低覆盖率保证。
- 适合对风险控制要求高的小数据场景，如医疗或金融预测。
分段共形预测提供无分布假设的预测集，具有保证的边际覆盖度。然而，在分段共形预测中，该保证仅在训练条件期望下成立：多次校准抽样中，平均覆盖率等于名义水平，但单次校准集的实际覆盖率可能显著波动。这种方差削弱了实际应用中的有效风险控制。本文提出小样本贝塔校正（SSBC），一种可直接插入的显著性水平调整方法，利用共形覆盖度的精确有限样本分布，提供概率性保证：在用户定义的概率下，部署的预测器能达到至少期望的覆盖率。
原文摘要 · Abstract (English)
Conformal prediction provides distribution-free prediction sets with guaranteed marginal coverage. However, in split conformal prediction this guarantee is training-conditional only in expectation: across many calibration draws, the average coverage equals the nominal level, but the realized coverage for a single calibration set may vary substantially. This variance undermines effective risk control in practical applications. Here we introduce the Small Sample Beta Correction (SSBC), a plug-and-play adjustment to the conformal significance level that leverages the exact finite-sample distribution of conformal coverage to provide probabilistic guarantees, ensuring that with user-defined probability over the calibration draw, the deployed predictor achieves at least the desired coverage.
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