Harnessing the Power of Vicinity-Informed Analysis for Classification under Covariate Shift

TL;DR

This paper introduces a vicinity-informed dissimilarity measure for classification under covariate shift, improving theoretical understanding and convergence rates in transfer learning scenarios with distribution differences.

Contribution

It proposes a novel local-structure-based dissimilarity measure that enhances excess error analysis under covariate shift, especially in support non-containment settings.

Findings

Faster or competitive convergence rates compared to previous methods

Effective in support non-containment scenarios

Bridges theoretical and empirical insights in transfer learning

Abstract

Transfer learning enhances prediction accuracy on a target distribution by leveraging data from a source distribution, demonstrating significant benefits in various applications. This paper introduces a novel dissimilarity measure that utilizes vicinity information, i.e., the local structure of data points, to analyze the excess error in classification under covariate shift, a transfer learning setting where marginal feature distributions differ but conditional label distributions remain the same. We characterize the excess error using the proposed measure and demonstrate faster or competitive convergence rates compared to previous techniques. Notably, our approach is effective in the support non-containment assumption, which often appears in real-world applications, holds. Our theoretical analysis bridges the gap between current theoretical findings and empirical observations in transfer learning, particularly in scenarios with significant differences between source and target distributions.