The Tractability Landscape of Sampling with Inexact Scores
A new preprint provides a tight characterization of the types of inexact score oracle access that allow for sampling with vanishing total variation bias in a standard target family. The main result demonstrates that any error weaker than the sub-Gaussian assumption precludes tractable unbiased sampling, extending previous work to be algorithm-agnostic and applicable to broader error models.
Why it matters: This result clarifies the theoretical limits of sampling with imperfect score estimates, which is important for understanding the reliability of score-based generative models such as diffusion models.
Full story at: arXiv Statistical ML ↗