Our paper [Fujikawa, Akimoto, Sakuma, and Fukuchi(2025)], titled Harnessing the Power of Vicinity-Informed Analysis for Classification under Covariate Shift, has been accepted at the 28th International Conference on Artificial Intelligence and Statistics. This paper introduces a novel dissimilarity measure that utilizes the local structure of data points to analyze excess error in classification under covariate shift. The proposed method achieves faster or competitive convergence rates compared to existing approaches and is particularly effective when the support non-containment assumption holds.