arXiv:2604. 05324v2 Announce Type: replace Abstract: Statistical evaluation aims to estimate the generalization performance of a model using held-out i.
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arXiv:2501. 18897v4 Announce Type: replace-cross Abstract: Generative models have achieved remarkable success across a range of applications, yet their evaluation still lacks principled uncertainty quantification.
arXiv:2402.04355v4 Announce Type: replace-cross Abstract: We propose a likelihood-free method for comparing two distributions given samples from each, with the goal of assessing the quality of genera...
arXiv:2609.01410v1 Announce Type: cross Abstract: Generative data augmentation is widely used to mitigate class imbalance, yet its theoretical effect on downstream generalization remains poorly under...
arXiv:2607. 24983v1 Announce Type: cross Abstract: Generative models are increasingly adopted in distributionally robust optimization (DRO), but existing approaches trade off model compatibility and adversarial structure: methods that accept arbitrary samplers do not restrict worst-case laws to a generator family, while generator-parameterized adversaries rely on model-specific access such as likelihoods, scores, or training data.
arXiv:2509. 21925v2 Announce Type: replace-cross Abstract: This paper investigates the theoretical behavior of generative models under finite training populations.
arXiv:2310. 11714v5 Announce Type: replace Abstract: Ranking generative models based on the fidelity and diversity of their outputs is required to identify the best generator in a group of candidate generative AI models.