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Mallory Goldstein

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#machine learning Preprint Sep 2026

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

This work establishes generative augmentation as a distributional perturbation process whose reliability can be quantified through Wasserstein-based measures and supported by finite-sample generalization guarantees.

Chathurika S. Abeykoon, M. Muia, Mallory Goldstein · 0 citations