This paper tackles the challenge of detecting unreliable behavior in regression algorithms, which may arise from intrinsic variability (e.g., aleatoric uncertainty) or modeling errors (e.g., model uncertainty). First, we formally introduce the notion of unreliability in regression, i.e., when the output of the regressor exceeds a specified discrepancy (or error). Then, using powerful tools for probabilistic modeling, we estimate the discrepancy density, and we measure its statistical diversity using our proposed metric for statistical dissimilarity. In turn, this allows us to derive a data-driven score that expresses the uncertainty of the regression outcome. We show empirical improvements in error detection for multiple regression tasks, consistently outperforming popular baseline approaches, and contributing to the broader field of uncertainty quantification and safe machine learning systems. Our code is available at https://zenodo.org/records/11281964.

该研究论文讨论了检测回归算法中的不可靠行为的挑战，首先正式介绍了回归中的不可靠性概念，然后利用概率建模工具估计误差密度并测量其统计差异性，从而得出表达回归结果不确定性的数据驱动评分。实验证明在多个回归任务中，该方法在错误检测方面的性能显著优于流行的基准方法，并为不确定性量化和安全机器学习系统的广泛领域做出贡献。

超越规范：检测回归模型中的预测误差