  In many machine learning applications, it is important for the model to
provide confidence scores that accurately capture its prediction uncertainty.
Although modern learning methods have achieved great success in predictive
accuracy, generating calibrated confidence scores remains a major challenge.
Mixup, a popular yet simple data augmentation technique based on taking convex
combinations of pairs of training examples, has been empirically found to
significantly improve confidence calibration across diverse applications.
However, when and how Mixup helps calibration is still a mystery. In this
paper, we theoretically prove that Mixup improves calibration in
\textit{high-dimensional} settings by investigating natural statistical models.
Interestingly, the calibration benefit of Mixup increases as the model capacity
increases. We support our theories with experiments on common architectures and
datasets. In addition, we study how Mixup improves calibration in
semi-supervised learning. While incorporating unlabeled data can sometimes make
the model less calibrated, adding Mixup training mitigates this issue and
provably improves calibration. Our analysis provides new insights and a
framework to understand Mixup and calibration.
