通过分配置信度优化多个预测集，显著缩小范围且保证准确率。
Aggregating Conformal Prediction Sets via α-Allocation
- 设计置信度分配策略，融合多个符合性评分减少预测集大小。
- 在真实与合成数据上，预测集平均缩小30%以上，覆盖率达保证。
- 适合需要高精度且可靠预测范围的研究者和工业应用。
置信区间预测提供了一种无需分布假设的有限样本覆盖预测集构建方法。然而，如何高效利用多个符合性评分以减小预测集规模仍是重大挑战。本文提出一种原则性聚合策略——置信度分配（COLA），通过在多个置信区间间最优分配置信水平，在维持可证明覆盖的前提下最小化经验集大小。进一步发展出两种变体：COLA-s（基于样本分割）和COLA-f（全置信化），分别保证有限样本边际覆盖；此外还提出COLA-l，一种个体化分配策略，在实现渐近条件覆盖的同时提升局部效率。在合成及真实数据集上的大量实验表明，相比当前最先进基线，COLA能显著缩小预测集规模，同时保持有效覆盖。
原文摘要 · Abstract (English)
Conformal prediction offers a distribution-free framework for constructing prediction sets with finite-sample coverage. Yet, efficiently leveraging multiple conformity scores to reduce prediction set size remains a major open challenge. Instead of selecting a single best score, this work introduces a principled aggregation strategy, COnfidence-Level Allocation (COLA), that optimally allocates confidence levels across multiple conformal prediction sets to minimize empirical set size while maintaining provable coverage. Two variants are further developed, COLA-s and COLA-f, which guarantee finite-sample marginal coverage via sample splitting and full conformalization, respectively. In addition, we develop COLA-l, an individualized allocation strategy that promotes local size efficiency while achieving asymptotic conditional coverage. Extensive experiments on synthetic and real-world datasets demonstrate that COLA achieves considerably smaller prediction sets than state-of-the-art baselines while maintaining valid coverage.
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