Abstract
Deep neural networks have achieved remarkable success across a variety of tasks, yet they often suffer from unreliable probability estimates. As a result, they can be overconfident in their predictions. Conformal Prediction (CP) offers a principled framework for uncertainty quantification, yielding prediction sets with rigorous coverage guarantees. Existing conformal training methods optimize for overall set size, but shaping the prediction sets in a class-conditional manner is not straightforward and typically requires prior knowledge of the data distribution. In this work, we introduce Class Adaptive Conformal Training (CaCT), which formulates conformal training as an augmented Lagrangian optimization problem that adaptively learns to shape prediction sets class-conditionally without making any distributional assumptions. Experiments on multiple benchmark datasets, including standard and long-tailed image recognition as well as text classification, demonstrate that CaCT consistently outperforms prior conformal training methods, producing significantly smaller and more informative prediction sets while maintaining the desired coverage guarantees.
Abstract (translated)
深度神经网络在多种任务中取得了显著的成功，但它们常常遭受不可靠的概率估计问题。因此，这些模型在其预测上可能过于自信。一致性预测（CP）提供了一个基于原则的不确定性量化框架，可以生成具有严格覆盖保证的预测集。现有的符合性训练方法优化的是整体集合大小，但是以条件分类的方式塑造预测集合并不简单，并且通常需要对数据分布有先验知识。 在本研究中，我们引入了类自适应一致性训练（CaCT），该方法将一致性的训练公式化为一个增强拉格朗日优化问题，在不做出任何关于分布假设的情况下，可以学习以条件分类的方式动态调整预测集合。在多个基准数据集上的实验表明，无论是标准和长尾图像识别还是文本分类任务，CaCT都能持续优于先前的一致性训练方法，生成更为精简且信息量更大的预测集合，同时保持预期的覆盖保证。
URL
https://arxiv.org/abs/2601.09522
