对比生成模型时，用新物理学习机提升检验效率
Comparing Generative Models with the New Physics Learning Machine
- 以分类方式实现两样本检验，替代传统统计方法
- 新物理学习机在高维数据中表现更优，但计算开销大
- 适合需要高精度的科学建模场景，如高能物理分析
生成模型在科学研究中的兴起，亟需新的评估方法来衡量其真实性。两样本假设检验——判断两组数据是否来自同一分布——为此提供自然框架。在大规模高维情形下，机器学习可突破传统统计方法局限。本文基于Grossi等（2025）框架，将高能物理文献中的新物理学习机（New Physics Learning Machine）与多种替代方法进行对比，验证其在分类式两样本检验中的表现。研究揭示了该方法的效率权衡及基于学习的方案带来的计算成本，并讨论了不同方法在不同应用场景下的优势。
原文摘要 · Abstract (English)
The rise of generative models for scientific research calls for the development of new methods to evaluate their fidelity. A natural framework for addressing this problem is two-sample hypothesis testing, namely the task of determining whether two data sets are drawn from the same distribution. In large-scale and high-dimensional regimes, machine learning offers a set of tools to push beyond the limitations of standard statistical techniques. In this work, we put this claim to the test by comparing a recent proposal from the high-energy physics literature, the New Physics Learning Machine, to perform a classification-based two-sample test against a number of alternative approaches, following the framework presented in Grossi et al. (2025). We highlight the efficiency tradeoffs of the method and the computational costs that come from adopting learning-based approaches. Finally, we discuss the advantages of the different methods for different use cases.
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