2 papers
stat.ML2026
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 (). In this wor…
stat.ML2025
A Hessian-Aware Stochastic Differential Equation for Modelling SGD
Xiang Li, Zebang Shen, Liang Zhang +1
Continuous-time approximation of Stochastic Gradient Descent (SGD) is a crucial tool to study its escaping behaviors from stationary points. However, existing stochastic differenti…