Active Diffusion-Based Inference for Ill-Posed Inverse Problems under Incomplete Priors
Many scientific and engineering applications require estimating unknown parameters from experimentally observable data -- an inverse problem that is inherently challenging due to nonlinearity, noise, and ill-posedness. In this paper, we propose an active diffusion-based inverse problem solver. A DM is trained to learn the mapping between the parameter space and the observable space. By iteratively detecting and correcting model misspecification through posterior uncertainty, the method discovers and learns the correct region of parameter space, even when initial training bounds exclude the true parameters. This provides a principled, Bayesian justification for adaptive domain augmentation...
| Authors | Jitao Xu, Nobuo Sato, Yaohang Li |
|---|---|
| Primary category | stat.ML |
| Published | 27 August 2026 |
| Last updated at source | 27 August 2026 |
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