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stat.ML2026
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 n…
stat.ML2025
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…
stat.ML2025
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 networks trained with algorithmic guarantees in this paper. We consider the setting where the trainin…