A Theoretical Framework for Statistical Evaluability of Generative Models
Statistical evaluation aims to estimate the generalization performance of a model using held-out i.i.d. test data sampled from the ground-truth distribution. In supervised learning settings such as classification, performance metrics such as error rate are well-defined, and test error reliably approximates population error given sufficiently large datasets. In contrast, evaluation is more challenging for generative models due to their open-ended nature: it is unclear which metrics are appropriate and whether such metrics can be reliably evaluated from finite samples. In this work, we introduce a theoretical framework for evaluating generative models and establish evaluability results for ...
| Authors | Shashaank Aiyer, Yishay Mansour, Shay Moran et al. |
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| Primary category | cs.LG |
| Published | 7 April 2026 |
| Last updated at source | 17 August 2026 |
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