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- W4313303723 abstract "We explore the ability of overparameterized shallow ReLU neural networks to learn Lipschitz, nondifferentiable, bounded functions with additive noise when trained by Gradient Descent (GD). To avoid the problem that in the presence of noise, neural networks trained to nearly zero training error are inconsistent in this class, we focus on the early-stopped GD which allows us to show consistency and optimal rates. In particular, we explore this problem from the viewpoint of the Neural Tangent Kernel (NTK) approximation of a GD-trained finite-width neural network. We show that whenever some early stopping rule is guaranteed to give an optimal rate (of excess risk) on the Hilbert space of the kernel induced by the ReLU activation function, the same rule can be used to achieve minimax optimal rate for learning on the class of considered Lipschitz functions by neural networks. We discuss several data-free and data-dependent practically appealing stopping rules that yield optimal rates." @default.
- W4313303723 created "2023-01-06" @default.
- W4313303723 creator A5041854594 @default.
- W4313303723 creator A5069856068 @default.
- W4313303723 date "2022-12-28" @default.
- W4313303723 modified "2023-10-05" @default.
- W4313303723 title "Learning Lipschitz Functions by GD-trained Shallow Overparameterized ReLU Neural Networks" @default.
- W4313303723 doi "https://doi.org/10.48550/arxiv.2212.13848" @default.
- W4313303723 hasPublicationYear "2022" @default.
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