An end-to-end framework for uncertainty-aware validation of generative models

The paper introduces SIGMA, an end‑to‑end framework that couples density estimation with principled uncertainty quantification for generative models. By using Gaussian mixture models and treating uncertainties as nuisance parameters in a new goodness‑of‑fit test, SIGMA converts a standard point‑null test into a composite test that accounts for statistical error of the fit. The authors demonstrate that incorporating uncertainties allows the same model to be trusted on test sets up to an order of magnitude larger than with conventional methods, using both a toy two‑dimensional example and a collider physics benchmark.

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