发表机构
Beijing Normal University; Renmin University of China; Zhejiang University（北京师范大学; 中国人民大学; 浙江大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
COINS提出任意阶段有效的序贯共形预测方法，通过共享拒绝计数预算协调校准，实现有限样本有效性并优于Bonferroni方法，应用于皮肤病诊断等场景。
AI 中文摘要
许多预测工作流在获取信息时会更新不确定性，并利用中间报告来决定是否停止或部署更多资源。我们在此设置下研究共形推断，将由此产生的预测序列视为推断对象。我们要求任意阶段有效性，这可以防止在任意被检查阶段出现覆盖不足，并使用过程级效用来评估序列对下游行动的支持效率。我们提出了一种用于构建任意阶段有效预测序列的通用结构理论。在该理论的指导下，我们开发了COINS，它通过仅在存活的增强观测中投入一个共同的有限样本拒绝计数预算来协调各阶段的校准。在可交换性条件下，COINS实现了有限样本的任意阶段有效性，并在每个阶段产生的预测集不大于其对应的Bonferroni方法。我们进一步开发了Vopt-COINS，它针对指定的过程级效用学习阶段分配，并针对异质获取路径和测试单元提出了分支和局部扩展。模拟实验和一项皮肤病诊断应用证实了任意阶段有效性，并展示了相对于Bonferroni和固定分配的优势。所提出的方法在有序分数聚合中（视为终端效用特例）也表现良好。
英文摘要
In clinical diagnosis, sequential screening, and cascaded prediction systems, information is often acquired in stages, with intermediate predictions guiding whether to act on the available evidence or defer action to gather further information. We study sequential predictive inference in these settings, aiming to construct a sequence of prediction sets that supports timely, resource-efficient decisions while remaining reliable throughout the acquisition process. Our objective is to maximize process-level utility subject to any-stage validity, which controls the probability of miscoverage anywhere in the prediction sequence. We develop COINS, a conformal framework for constructing such sequences, and establish a universality theorem identifying the common exclusion structure underlying all nested, calibration-symmetric, any-stage valid prediction sequences. Building on COINS, we develop Vopt-COINS for data-driven optimization of prediction sequences under task-specific utilities based on prediction-set size, stopping time and acquisition costs. Branchwise and localized extensions further support individualized acquisition and calibration. Numerical studies demonstrate the effectiveness and practical benefits of the proposed framework for reliable, resource-efficient sequential prediction.
