This paper develops novel conformal prediction methods for classification tasks that can automatically adapt to random label contamination in the calibration sample, enabling more informative prediction sets with stronger coverage guarantees compared to state-of-the-art approaches. This is made possible by a precise theoretical characterization of the effective coverage inflation (or deflation) suffered by standard conformal inferences in the presence of label contamination, which is then made actionable through new calibration algorithms. Our solution is flexible and can leverage different modeling assumptions about the label contamination process, while requiring no knowledge about the data distribution or the inner workings of the machine-learning classifier. The advantages of the proposed methods are demonstrated through extensive simulations and an application to object classification with the CIFAR-10H image data set.

该研究开发了新的遵循性预测方法，用于分类任务，可以自动适应标签污染，实现比现有方法更具信息量的预测集，并提供更强的覆盖保证。通过对标签污染下标准遵循性推断所面临的准确理论特征进行精确刻画，并通过新的校准算法实施行动。我们的解决方法灵活，并且可以利用关于标签污染过程的不同建模假设，同时无需关于数据分布或机器学习分类器内部工作的任何知识。通过广泛的模拟实验和对CIFAR-10H图像数据集的对象分类应用来证明了所提出方法的优势。

带有噪声标签的自适应适合性分类