{"ID":2842701,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.09118","arxiv_id":"2511.09118","title":"Learning to Validate Generative Models: a Goodness-of-Fit Approach","abstract":"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.","short_abstract":"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, mak...","url_abs":"https://arxiv.org/abs/2511.09118","url_pdf":"https://arxiv.org/pdf/2511.09118v2","authors":"[\"Pietro Cappelli\",\"Gaia Grosso\",\"Marco Letizia\",\"Humberto Reyes-González\",\"Marco Zanetti\"]","published":"2025-11-12T08:47:08Z","proceeding":"stat.ML","tasks":"[\"stat.ML\",\"cs.LG\",\"hep-ex\",\"hep-ph\"]","methods":"[]","has_code":false}