去相关理论的警示
A Cautionary Tale of Decorrelating Theory Uncertainties
摘要：
已经提出了各种技术来训练独立于给定特征的机器学习分类器。虽然这可能是实现背景估计的必要技术，但它也可能有助于减少不确定性。我们仔细研究理论的不确定性，这通常没有统计的起源。我们将提供两点（碎片建模）和连续（高阶校正）不确定性的明确例子，其中去相关显着降低了表观不确定性，而实际的不确定性要大得多。这些结果表明，在使用这些类型的不确定性去相关时，只要我们没有完全分解成统计上有意义的成分，就应该谨慎。
英文摘要：
A variety of techniques have been proposed to train machine learning classifiers that are independent of a given feature. While this can be an essential technique for enabling background estimation, it may also be useful for reducing uncertainties. We carefully examine theory uncertainties, which typically do not have a statistical origin. We will provide explicit examples of two-point (fragmentation modeling) and continuous (higher-order corrections) uncertainties where decorrelating significantly reduces the apparent uncertainty while the actual uncertainty is much larger. These results suggest that caution should be taken when using decorrelation for these types of uncertainties as long as we do not have a complete decomposition into statistically meaningful components.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
