22 citations · 45 across the 13 of their papers we have counts for
13 papers
Learning Orthogonal Multi-Index Models Beyond Small Initialization: Incremental Learning, Competitive Dynamics and Symmetry
Mo Zhou, Weihang Xu, Simon S. Du +1
Recent work has identified incremental learning in shallow networks trained on single-index and multi-index models. However, existing analyses often rely on simplifying settings, s…
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…
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…
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…
Depth Separation with Multilayer Mean-Field Networks
Yunwei Ren, Mo Zhou, Rong Ge
Depth separation -- why a deeper network is more powerful than a shallower one -- has been a major problem in deep learning theory. Previous results often focus on representation p…
Implicit Regularization Leads to Benign Overfitting for Sparse Linear Regression
Mo Zhou, Rong Ge
In deep learning, often the training process finds an interpolator (a solution with 0 training loss), but the test loss is still low. This phenomenon, known as benign overfitting,…