Data-dependent Algorithmic Robustness Analysis of Pairwise Learning

Abstract

This paper develops a new framework to understand generalization for pairwise learning problems, which covers many popular machine learning problems as specific examples. By integrating robust optimization principles with pairwise loss structures, we establish data-dependent generalization bounds that significantly improve over existing approaches. Our method overcomes key limitations of prior work by leveraging observable training data properties rather than restrictive theoretical assumptions. This results in tighter performance guarantees that better reflect real-world learning behavior, particularly for complex datasets with dependent training pairs.