针对无法获取精确标签的回归问题，提出新型损失与极小极大学习框架。
Learning from Interval Targets
- 设计适配区间目标的损失函数，放宽了对模型可实现性的要求。
- 引入极小极大学习策略，在区间内最坏情况仍能保持优异性能。
- 实验验证在真实数据集上达到当前最优效果，适合不确定场景建模。
我们研究了带有区间目标的回归问题，即仅能获得目标值的上下界，而非精确标签。这类问题常因标签获取成本过高或存在固有不确定性而出现。由于缺乏精确目标，传统回归损失函数无法使用。首先，我们研究了适用于区间目标的损失函数，基于假设类的光滑性建立了非渐近泛化界，显著放松了以往对可实现性和小歧义度的强假设。其次，提出一种新的极小极大学习范式：在给定区间内最小化最坏情况（最大化）的目标标签。该最大化问题非凸，但通过引入光滑性约束可实现良好性能。最后，在多个真实数据集上进行了广泛实验，结果表明所提方法达到当前最佳水平。
原文摘要 · Abstract (English)
We study the problem of regression with interval targets, where only upper and lower bounds on target values are available in the form of intervals. This problem arises when the exact target label is expensive or impossible to obtain, due to inherent uncertainties. In the absence of exact targets, traditional regression loss functions cannot be used. First, we study the methodology of using a loss functions compatible with interval targets, for which we establish non-asymptotic generalization bounds based on smoothness of the hypothesis class that significantly relaxing prior assumptions of realizability and small ambiguity degree. Second, we propose a novel min-max learning formulation: minimize against the worst-case (maximized) target labels within the provided intervals. The maximization problem in the latter is non-convex, but we show that good performance can be achieved with the incorporation of smoothness constraints. Finally, we perform extensive experiments on real-world datasets and show that our methods achieve state-of-the-art performance.
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