On the Reliability of Generative Augmentation: A Wasserstein-Based Theoretical and Empirical Study

Generative data augmentation is widely used to mitigate class imbalance, yet its theoretical effect on downstream generalization remains poorly understood. In this work, we develop a statistical framework for conditional generative augmentation and analyze its impact on classification risk. We forma