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Conformal prediction under ambiguous ground truth

Summary

This research paper proposes a novel Monte Carlo Conformal Prediction (CP) method to address uncertainty quantification in classification tasks with ambiguous ground truth labels. Standard CP methods often rely on "voted" labels derived from aggregated expert opinions, ignoring inherent label uncertainty. The proposed Monte Carlo CP leverages expert opinions to create a non-degenerate label distribution, generating synthetic pseudo-labels to improve coverage guarantees. The authors demonstrate the method's effectiveness through experiments on skin condition classification, showing improvements over existing CP techniques in handling ambiguous labels. The paper also explores extensions to multi-label classification and robust CP with data augmentation.

本研究提出了一种新型的蒙特卡罗符合性预测（Monte Carlo Conformal Prediction, CP）方法，用于解决具有模糊真实标签的分类任务中的不确定性量化问题。标准的 CP 方法通常依赖于通过聚合专家意见得出的“投票”标签，忽略了标签固有的不确定性。所提的蒙特卡罗 CP 利用专家意见创建一个非退化的标签分布，生成合成伪标签，以提高覆盖保证。作者通过皮肤病分类实验验证了该方法的有效性，表明其在处理模糊标签时相比现有的 CP 技术有所改进。论文还探讨了该方法在多标签分类和基于数据增强的稳健 CP 中的扩展应用。

原文链接：https://arxiv.org/abs/2307.09302