A Short Survey on Importance Weighting for Machine Learning

Published: 14 May 2024, Last Modified: 14 May 2024Accepted by TMLREveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: 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.
Certifications: Survey Certification
Submission Length: Long submission (more than 12 pages of main content)
Assigned Action Editor: ~Satoshi_Hara1
Submission Number: 2379