提出可应对分布偏移的校准预测系统，提升模型在非独立同分布下的可靠性。
Generalized Conformal Predictive Systems Under Distributional Shifts
- 用观测权重编码分布偏移，构建条件有效的预测机制
- 偏移越强预测区间越宽，样本量越大则区间越窄
- 适用于数据分布变化场景，如生物分子设计与协变量偏移
传统的置信预测系统（CPS）在交换性假设下输出校准的累积分布函数带。本文通过引入观测特定的置换权重，将广义CPS扩展至非交换性设置，使预测系统能够感知分布偏移。只要测试点在无序样本条件下是观测原子的加权抽取，该系统即保持有效性。由于权重通常需估计，我们引入权重不确定性盒，构建具有有限样本或渐近置信保证的稳健CPS包络。推导了符合度度量CPS、分箱法和等距分布回归的高效计算方法。在协变量偏移与反馈驱动的生物分子设计实验中，结果表明预测带在偏移增强时变宽，随样本量增加而收紧。
原文摘要 · Abstract (English)
Conformal predictive systems (CPS) output calibrated bands of CDFs under exchangeability. We extend generalized CPS to non-exchangeable settings by encoding distributional shifts through observation-specific permutation weights. This yields shift-aware predictive systems that remain valid whenever the test point is, conditionally on the unordered sample, a weighted draw from the observed atoms. Since such weights are typically estimated, we introduce weight-uncertainty boxes and construct robust CPS envelopes with finite-sample or asymptotic confidence guarantees. We derive efficient computation for conformity-measure CPS, conformal binning, and conformal isotonic distributional regression. Experiments under covariate shift and feedback-driven biomolecular design show calibrated predictive bands that widen under stronger shifts and tighten as sample size increases.
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