研究标签噪声下个体决策的后悔问题，提出预测个体失误概率的新方法。
Regretful Decisions under Label Noise
- 通过构建无噪声数据的多种可能实现，估算个体层面出错概率。
- 发现现有方法虽整体准确，但个体风险如随机抽奖。
- 适用于医疗等高风险决策场景，提升模型可信赖度。
机器学习模型常用于影响个人的重要决策，如疾病筛查或治疗反应评估，但训练数据普遍存在标签噪声。本文研究标签噪声对个体决策的影响，引入‘后悔’概念——衡量因标签噪声导致的意外错误数量。研究表明，现有标签噪声处理方法虽在总体上表现良好，却使个体面临不可预测的错误风险。为此，提出一种通用方法：通过对数据集可能的无噪声版本进行建模，估计个体层面的出错概率。该方法在临床预测任务中进行了全面实证研究，揭示忽视错误预测会损害模型可靠性与实际应用。本文通过预见并规避后悔性决策，有效应对这一挑战。
原文摘要 · Abstract (English)
Machine learning models are routinely used to support decisions that affect individuals -- be it to screen a patient for a serious illness or to gauge their response to treatment. In these tasks, we are limited to learning models from datasets with noisy labels. In this paper, we study the instance-level impact of learning under label noise. We introduce a notion of regret for this regime, which measures the number of unforeseen mistakes due to noisy labels. We show that standard approaches to learning under label noise can return models that perform well at a population-level while subjecting individuals to a lottery of mistakes. We present a versatile approach to estimate the likelihood of mistakes at the individual-level from a noisy dataset by training models over plausible realizations of datasets without label noise. This is supported by a comprehensive empirical study of label noise in clinical prediction tasks. Our results reveal how failure to anticipate mistakes can compromise model reliability and adoption -- we demonstrate how we can address these challenges by anticipating and avoiding regretful decisions.
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