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stat.ML2026
On the Reliability of Generative Augmentation: A Wasserstein-Based Theoretical and Empirical Study
Chathurika S Abeykoon, Mathias Nthiani Muia, Mallory Goldstein
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…
stat.ML2023
Generalization error property of infoGAN for two-layer neural network
Mahmud Hasan, Mathias Muia
Information Maximizing Generative Adversarial Network (infoGAN) can be understood as a minimax problem involving two neural networks: discriminators and generators with mutual info…