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 formalize augmentation as a distribution-mixing process and show that the resulting risk distortion is controlled by both the augmentation strength and the class-conditional Wasserstein discrepancy between real and generated distributions. We further derive a capacity-dependent generalization bound based on Rademacher complexity, revealing an explicit trade-off between hypothesis complexity, augment...
| Authors | Chathurika S Abeykoon, Mathias Nthiani Muia, Mallory Goldstein |
|---|---|
| Primary category | stat.ML |
| Published | 1 September 2026 |
| Last updated at source | 1 September 2026 |
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