Generative models have shown significant promise in critical domains such as medical diagnosis, autonomous driving, and climate science, where reliable decision-making hinges on accurate uncertainty quantification. While probabilistic conformal prediction (PCP) offers a powerful framework for this purpose, its coverage efficiency -- the size of the uncertainty set -- is limited when dealing with complex underlying distributions and a finite number of generated samples. In this paper, we propose a novel PCP framework that enhances efficiency by first vectorizing the non-conformity scores with ranked samples and then optimizing the shape of the prediction set by varying the quantiles for samples at the same rank. Our method delivers valid coverage while producing discontinuous and more efficient prediction sets, making it particularly suited for high-stakes applications. We demonstrate the effectiveness of our approach through experiments on both synthetic and real-world datasets.

本研究解决了在复杂分布和有限样本情况下，概率顺应预测（PCP）覆盖效率不足的问题。作者提出了一种新颖的PCP框架，通过向量化非顺应性分数并优化预测集形状，显著提高了效率。实验表明，该方法在合成和真实数据集上均表现出色，尤其适合高风险应用。

优化向量化非顺应性分数的概率顺应预测