通过可训练变换提升条件覆盖精度，增强多输出预测可靠性。
Rectifying Conformity Scores for Better Conditional Coverage
- 用可训练变换优化置信集的符合度得分，提升条件覆盖
- 理论证明量化估计精度影响条件有效性，优于传统方法
- 在多输出场景中表现更优，适合需要高可靠推断的应用
我们提出一种新方法，在分拆式合取预测框架内生成置信集。该方法对任意给定的符合度得分进行可训练变换，以改善条件覆盖，同时保证精确的边际覆盖。变换基于符合度得分的条件分位数估计。所提方法特别适用于多输出问题中的自适应置信集构建，而标准合取分位数回归在此类问题中适用性有限。我们建立了理论界，刻画了分位数估计准确性对近似条件有效性的影响，这不同于传统合取预测方法仅提供边际覆盖的边界。实验表明，该方法对局部数据结构高度自适应，在条件覆盖性能上优于现有方法，提升了各类应用中统计推断的可靠性。
原文摘要 · Abstract (English)
We present a new method for generating confidence sets within the split conformal prediction framework. Our method performs a trainable transformation of any given conformity score to improve conditional coverage while ensuring exact marginal coverage. The transformation is based on an estimate of the conditional quantile of conformity scores. The resulting method is particularly beneficial for constructing adaptive confidence sets in multi-output problems where standard conformal quantile regression approaches have limited applicability. We develop a theoretical bound that captures the influence of the accuracy of the quantile estimate on the approximate conditional validity, unlike classical bounds for conformal prediction methods that only offer marginal coverage. We experimentally show that our method is highly adaptive to the local data structure and outperforms existing methods in terms of conditional coverage, improving the reliability of statistical inference in various applications.
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