On the Decomposition of Covariate Shift Assumption for the Set-to-Set Matching

Abstract

The task of set matching, which models the quality of matching between pairs of sets, is expected to have a wide range of practical applications. However, many existing methods that address this task assume that the training and testing distributions are identical, which is frequently violated in realworld scenarios. To address this issue, the covariate shift assumption focuses on the shift in the distribution of covariates between the training and testing datasets. While several studies have analyzed this assumption for vector inputs, there is a lack of research on similar assumptions when the input is a pair of sets. In this study, we refine and redefine the covariate shift assumption in set matching and analyze how models perform under these conditions.