Importance weighting is a fundamental procedure in statistics and machine learning that weights the objective function or probability distribution based on the importance of the instance in some sense. The simplicity and usefulness of the idea has led to many applications of importance weighting. For example, it is known that supervised learning under an assumption about the difference between the training and test distributions, called distribution shift, can guarantee statistically desirable properties through importance weighting by their density ratio. This survey summarizes the broad applications of importance weighting in machine learning and related research.

翻译：重要性加权是统计学和机器学习中的基本方法，它基于实例在某种意义上的重要性，对目标函数或概率分布进行加权。该思想的简洁性和实用性使其在许多领域得到应用。例如，在训练分布与测试分布存在差异（即分布漂移）的假设下，通过密度比例进行重要性加权，可使监督学习获得统计上理想的特性。本综述总结了重要性加权在机器学习中的广泛应用及相关研究。