When Scores Learn Geometry: Rate Separations under the Manifold Hypothesis

Xiang Li, Zebang Shen, Ya-Ping Hsieh +1

Score-based methods, such as diffusion models and Bayesian inverse problems, are often interpreted as learning the data distribution in the low-noise limit ($\sigma \to 0$). In this work…