arXiv Machine Learning July 24, 2026 By Zijun Gao, Yan Sun, Han Su

Statistical Inference for Generative Model Comparison

Read the original on arXiv Machine Learning →

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.

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benchmarks

arXiv Machine Learning

Jun 2

A Theoretical Framework for Statistical Evaluability of Generative Models

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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Sep 1

PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation

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...

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Are Two Datasets Close Enough With Statistical Significance? A Kernel Distributional Closeness Testing Approach

arXiv:2507. 12843v3 Announce Type: replace Abstract: Are two distributions close to each other with statistical significance?

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Sep 2

On the Reliability of Generative Augmentation: A Wasserstein-Based Theoretical and Empirical Study

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diffusion data-engineering

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A Unified Risk View of Uncertainty: Posterior Risk for Disentanglement and Evaluation Beyond Proxies

arXiv:2608. 05995v1 Announce Type: new Abstract: Reliable uncertainty estimates are critical in safety-sensitive applications, where understanding the sources of predictive uncertainty is essential.

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benchmarks safety

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MGI: Member vs Generated Inference

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By Bihe Zhao, Michel Meintz, Juangui Xu, Franziska Boenisch, Adam Dziedzic

diffusion

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A Theoretical Framework for Statistical Evaluability of Generative Models

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