发表机构
Ewha Womans University; Purdue University（梨花女子大学; 普渡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究将GAN训练的切换时机判定转化为序列假设检验问题，提出基于E-过程的自适应训练流程，在多模态合成分布与图像数据集上表现优于或匹配最优固定比例基线。
AI 中文摘要
现代数据科学日益催生无法自然地用预设统计模型内参数表述的假设检验问题，动态评估优化算法是重要实例，训练期间需判定进一步更新是否有益或算法是否应切换至不同阶段，该问题在随机极小极大优化中尤为关键。生成对抗网络（GANs）是典型案例，其训练需反复判定何时切换判别器与生成器的更新时机，而现有方法通常依赖固定更新比例或启发式准则。我们将该切换问题表述为序列假设检验，并开发基于E-过程的自适应训练流程：在判别器更新阶段，一个E-过程检验原假设，即判别器诱导的经验数据分布与生成器分布间的分离度仍低于目标水平；在生成器更新阶段（判别器固定），第二个E-过程检验反向原假设，即该分离度仍高于刷新水平。基于观测到的训练样本，我们证明新鲜经验索引与潜在采样可生成条件E-值，可累积为E-过程，在自适应模型更新与依赖数据的切换下提供任意时刻有效的I类错误控制。在多模态合成分布与图像基准数据集上，所提方法在多种广泛使用的GAN目标函数下，与最优固定比例基线方法表现相当或更优。
英文摘要
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 ratios or heuristic criteria. We formulate this switching problem as sequential hypothesis testing and develop an e-process-based adaptive training procedure. During discriminator updates, one e-process tests the null that the discriminator-induced separation between the empirical data distribution and the generator law remains below a target level. During generator updates, with the discriminator fixed, a second e-process tests the reverse null that this separation remains above a refresh level. Conditional on the observed training sample, we prove that fresh empirical indices and latent draws yield conditional e-values that can be accumulated into e-processes, providing anytime-valid Type I error control under adaptive model updates and data-dependent switching. Across multimodal synthetic distributions and image benchmark datasets, the proposed method matches or outperforms the best fixed-ratio baselines under several widely used GAN objectives.
