A Short Survey on Importance Weighting for Machine Learning
It provides a concise overview for researchers and practitioners, but is incremental as it compiles existing knowledge without new findings.
This survey summarizes the broad applications of importance weighting in machine learning, addressing its role in handling distribution shifts and ensuring statistically desirable properties in supervised learning.
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.