This paper studies the problem of post-hoc calibration of machine learning classifiers. We introduce the following desiderata for uncertainty calibration: (a) accuracy-preserving, (b) data-efficient, and (c) high expressive power. We show that none of the existing methods satisfy all three requirements, and demonstrate how Mix-n-Match calibration strategies (i.e., ensemble and composition) can help achieve remarkably better data-efficiency and expressive power while provably preserving classification accuracy of the original classifier. We also show that existing calibration error estimators (e.g., histogram-based ECE) are unreliable especially in small-data regime. Therefore, we propose an alternative data-efficient kernel density-based estimator for a reliable evaluation of the calibration performance and prove its asymptotically unbiasedness and consistency.

本文研究了机器学习分类器的后续校准问题，提出了三种不同的校准要求，并通过Mix-n-Match校准策略实现了数据效率和表达能力的显着提高，并提出了一种基于核密度估计的可靠评估方法，结果表明我们的方法在大多数实验设定中表现优于现有的解决方案。

混合和组合方法：用于深度学习中不确定性校准的集成和组合方法