用学习型拟合检验法验证生成模型，高效识别高维科学数据中的偏差。
Learning to Validate Generative Models: a Goodness-of-Fit Approach
- 基于奈曼-皮尔逊构造，设计可扩展的生成模型拟合检验方法
- 在高维高能物理数据上准确检出模型偏差，支持故障定位
- 适合需要严格验证生成模型的科研场景，如粒子物理模拟
生成模型在科学工作流中日益重要，但其系统性使用和解读需要通过严格的验证来理解其局限性。传统方法在处理高维数据时面临可扩展性差、统计功效低或解释性不足的问题，难以在真实高维科学场景中认证生成模型的可靠性。本文提出基于新物理学习机（NPLM）的学习型拟合检验方法，该方法受奈曼-皮尔逊构造启发，用于验证高维科学数据训练的生成网络。我们在两个基准案例中验证了NPLM的性能：一是随维度增加的高斯混合模型生成器，二是公开的端到端高能物理碰撞事件生成模型FlowSim。结果表明，NPLM不仅具备强大的验证能力，还能定位数据中建模不佳的区域。
原文摘要 · Abstract (English)
Generative models are increasingly central to scientific workflows, yet their systematic use and interpretation require a proper understanding of their limitations through rigorous validation. Classic approaches struggle with scalability, statistical power, or interpretability when applied to high-dimensional data, making it difficult to certify the reliability of these models in realistic, high-dimensional scientific settings. Here, we propose the use of the New Physics Learning Machine (NPLM), a learning-based approach to goodness-of-fit testing inspired by the Neyman--Pearson construction, to test generative networks trained on high-dimensional scientific data. We demonstrate the performance of NPLM for validation in two benchmark cases: generative models trained on mixtures of Gaussian models with increasing dimensionality, and a public end-to-end model, known as FlowSim, developed to generate high-energy physics collision events. We demonstrate that the NPLM can serve as a powerful validation method while also providing a means to diagnose sub-optimally modeled regions of the data.
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