5 papers
Gradient Descent with Projection Finds Over-Parameterized Neural Networks for Learning Low-Degree Polynomials with Nearly Minimax Optimal Rate
Yingzhen Yang, Ping Li
We study the problem of learning a low-degree spherical polynomial of degree defined on the unit sphere in $\RR^d$ by training an over-parameterized two-layer ne…
Maximum packings in graphs forbidding given rainbow cycles
Ping Li, Yang Yang
For graphs and , -multicolor Turán number of , denoted by , is the maximum number of edge-disjoint copies of in an -vertex graph such that t…
Gradient Descent Finds Over-Parameterized Neural Networks with Sharp Generalization for Nonparametric Regression
Yingzhen Yang, Ping Li
We study nonparametric regression by an over-parameterized two-layer neural network trained by gradient descent (GD) in this paper. We show that, if the neural network is trained b…
Sharp Generalization for Nonparametric Regression in Interpolation Space by Over-Parameterized Neural Networks Trained with Preconditioned Gradient Descent and Early Stopping
Yingzhen Yang, Ping Li
We study nonparametric regression using an over-parameterized two-layer neural network trained with algorithmic guarantees in this paper. We consider the setting where the training…
Sketching for Convex and Nonconvex Regularized Least Squares with Sharp Guarantees
Yingzhen Yang, Ping Li
Randomized algorithms are important for solving large-scale optimization problems. In this paper, we propose a fast sketching algorithm for least square problems regularized by con…