通过精确建模得分分布，让排名预测更高效且可靠。
Distribution-informed Efficient Conformal Prediction for Full Ranking
- 基于校准项的相对排序推导绝对排序的精确分布
- 平均预测集大小减少36%，同时保证覆盖率
- 适合需要高精度排序置信度的推荐系统场景
在真实应用中，量化排序模型的不确定性至关重要。现有方法在全排序场景下使用置信区间预测法，依据校准项的相对排序构建测试项绝对排序的预测集。然而，依赖非符合性得分的上界导致结果过于保守，预测集过大。为此，我们提出分布感知的置信排序（DCR），通过推导非符合性得分的精确分布来生成高效预测集。特别地，我们发现校准项的绝对排序在给定其相对排序条件下服从负超几何分布。DCR利用该分布推导非符合性得分分布，并确定置信阈值。理论上证明了在温和假设下，DCR比基线方法更高效且仍保证有效覆盖。大量实验表明，DCR可将平均预测集大小降低最高达36%，同时保持有效覆盖率。
原文摘要 · Abstract (English)
Quantifying uncertainty is critical for the safe deployment of ranking models in real-world applications. Recent work offers a rigorous solution using conformal prediction in a full ranking scenario, which aims to construct prediction sets for the absolute ranks of test items based on the relative ranks of calibration items. However, relying on upper bounds of non-conformity scores renders the method overly conservative, resulting in substantially large prediction sets. To address this, we propose Distribution-informed Conformal Ranking (DCR), which produces efficient prediction sets by deriving the exact distribution of non-conformity scores. In particular, we find that the absolute ranks of calibration items follow Negative Hypergeometric distributions, conditional on their relative ranks. DCR thus uses the rank distribution to derive non-conformity score distribution and determine conformal thresholds. We provide theoretical guarantees that DCR achieves improved efficiency over the baseline while ensuring valid coverage under mild assumptions. Extensive experiments demonstrate the superiority of DCR, reducing average prediction set size by up to 36%, while maintaining valid coverage.
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