标题： 分割共形分类与无监督校准 显示英文标题

标题： Split Conformal Classification with Unsupervised Calibration

摘要： 用于分割共形预测的方法利用校准样本将任何预测规则转换为符合目标覆盖概率的集合预测规则。 现有方法在计算成本最小的情况下提供了非常强大的性能保证。 然而，它们需要使用由与训练中使用的样本不同的标记示例组成的校准样本。 这一要求可能非常不便，因为它阻止了所有标记示例用于训练，并可能需要单独获取额外的标签仅用于校准。 本文提出了一种针对分类任务的带有无监督校准的分割共形预测有效方法。 在所提出的方法中，使用无监督校准样本以及之前用于学习分类规则的监督训练样本获得集合预测规则。 理论和实验结果表明，所提出的方法可以在性能保证和计算效率适度下降的情况下达到与监督校准相当的性能。 显示英文摘要

摘要： 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.