Learning to Validate Generative Models: a Goodness-of-Fit Approach
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arXiv
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| Format: | Preprint |
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2025
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| author | Cappelli, Pietro Grosso, Gaia Letizia, Marco Reyes-González, Humberto Zanetti, Marco |
| author_facet | Cappelli, Pietro Grosso, Gaia Letizia, Marco Reyes-González, Humberto Zanetti, Marco |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_09118 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning to Validate Generative Models: a Goodness-of-Fit Approach Cappelli, Pietro Grosso, Gaia Letizia, Marco Reyes-González, Humberto Zanetti, Marco Machine Learning High Energy Physics - Experiment High Energy Physics - Phenomenology 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. |
| title | Learning to Validate Generative Models: a Goodness-of-Fit Approach |
| topic | Machine Learning High Energy Physics - Experiment High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2511.09118 |