3 papers
cs.LG2025
Convergence Dynamics of Over-Parameterized Score Matching for a Single Gaussian
Yiran Zhang, Weihang Xu, Mo Zhou +2
Score matching has become a central training objective in modern generative modeling, particularly in diffusion models, where it is used to learn high-dimensional data distribution…
cs.LG2025
Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures
Mo Zhou, Weihang Xu, Maryam Fazel +1
Learning Gaussian Mixture Models (GMMs) is a fundamental problem in statistics and machine learning, with the Expectation-Maximization (EM) algorithm and its popular variant gradie…
cs.LG2024
How Does Gradient Descent Learn Features -- A Local Analysis for Regularized Two-Layer Neural Networks
Mo Zhou, Rong Ge
The ability of learning useful features is one of the major advantages of neural networks. Although recent works show that neural network can operate in a neural tangent kernel (NT…