Deciding When to Switch: E-Processes for Adaptive Minimax Training for Generative Adversarial Nets
Modern data science increasingly gives rise to hypothesis-testing problems that are not naturally formulated in terms of parameters within prespecified statistical models. One important example is the dynamic evaluation of optimization algorithms, where decisions must be made during training about whether further updates remain beneficial or the algorithm should switch to a different phase. This issue is particularly relevant in stochastic min-max optimization. Generative adversarial networks (GANs) provide a canonical example, as their training requires repeated decisions about when to switch between discriminator and generator updates, yet existing methods typically rely on fixed update...
| Authors | Hyunjoo Kim, Sicheng Wu, Agastya Venkatraman et al. |
|---|---|
| Primary category | stat.ML |
| Published | 10 August 2026 |
| Last updated at source | 10 August 2026 |
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