Methods for split conformal prediction leverage calibration samples to transform any prediction rule into a set-prediction rule that complies with a target coverage probability. Existing methods provide remarkably strong performance guarantees with minimal computational costs. However, they require to use calibration samples composed by labeled examples different to those used for training. This requirement can be highly inconvenient, as it prevents the use of all labeled examples for training and may require acquiring additional labels solely for calibration. This paper presents an effective methodology for split conformal prediction with unsupervised calibration for classification tasks. In the proposed approach, set-prediction rules are obtained using unsupervised calibration samples together with supervised training samples previously used to learn the classification rule. Theoretical and experimental results show that the presented methods can achieve performance comparable to that with supervised calibration, at the expenses of a moderate degradation in performance guarantees and computational efficiency.

翻译：分割共形预测方法利用校准样本将任意预测规则转化为符合目标覆盖概率的集合预测规则。现有方法以极低的计算成本提供了极为强大的性能保证。然而，这些方法要求使用与训练样本不同的带标签样本作为校准集。这一要求可能带来极大不便：既阻碍了将所有带标签样本用于训练，又可能需要仅为校准目的而获取额外标签。本文提出了一种用于分类任务的无监督校准分割共形预测的有效方法。该方案通过无监督校准样本与先前用于学习分类规则的监督训练样本相结合，获得集合预测规则。理论与实验结果表明，所提方法在性能保证和计算效率方面仅承受适度折损，即可达到与监督校准相当的性能水平。