A probabilistic model is said to be calibrated if its predicted probabilities match the corresponding empirical frequencies. Calibration is important for uncertainty quantification and decision making in safety-critical applications. While calibration of classifiers has been widely studied, we find that calibration is brittle and can be easily lost under minimal covariate shifts. Existing techniques, including domain adaptation ones, primarily focus on prediction accuracy and do not guarantee calibration neither in theory nor in practice. In this work, we formally introduce the problem of calibration under domain shift, and propose an importance sampling based approach to address it. We evaluate and discuss the efficacy of our method on both real-world datasets and synthetic datasets.

中文翻译：

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协变量偏移下的无监督校准

如果概率模型的预测概率与相应的经验频率匹配，则称该概率模型已校准。校准对于安全关键应用中的不确定性量化和决策制定非常重要。虽然分类器的校准已被广泛研究，但我们发现校准很脆弱，并且在最小协变量移位下很容易丢失。现有技术，包括域自适应技术，主要关注预测准确性，并且在理论上和实践中都不保证校准。在这项工作中，我们正式介绍了域转移下的校准问题，并提出了一种基于重要性采样的方法来解决它。我们评估并讨论了我们的方法在真实世界数据集和合成数据集上的有效性。