Recently, learning with soft labels has been shown to achieve better
performance than learning with hard labels in terms of model generalization,
calibration, and robustness. However, collecting pointwise labeling confidence
for all training examples can be challenging and time-consuming in real-world
scenarios. This paper delves into a novel weakly supervised binary
classification problem called confidence-difference (ConfDiff) classification.
Instead of pointwise labeling confidence, we are given only unlabeled data
pairs with confidence difference that specifies the difference in the
probabilities of being positive. We propose a risk-consistent approach to
tackle this problem and show that the estimation error bound achieves the
optimal convergence rate. We also introduce a risk correction approach to
mitigate overfitting problems, whose consistency and convergence rate are also
proven. Extensive experiments on benchmark data sets and a real-world
recommender system data set validate the effectiveness of our proposed
approaches in exploiting the supervision information of the confidence
difference.

使用软标签进行学习在模型泛化、校准和鲁棒性方面表现更好。本文提出了一种称为置信度差异分类的弱监督二分类问题，并提出了一种风险一致的方法来解决该问题。通过大量的实验证明了该方法在利用置信度差异的监督信息方面的有效性。