Establishes first minimax-optimal generalization rates for GD and SGD on deep ReLU networks under polynomial width scaling in the NTK regime.
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NTK neural networks achieve minimax optimal adversarial regression rates in Sobolev spaces using gradient flow with early stopping, but minimum norm interpolants are vulnerable in the overfitting regime.
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Optimal Rates for Generalization of Gradient Descent Methods with Deep Neural Networks
Establishes first minimax-optimal generalization rates for GD and SGD on deep ReLU networks under polynomial width scaling in the NTK regime.
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Adversarial Robustness of NTK Neural Networks
NTK neural networks achieve minimax optimal adversarial regression rates in Sobolev spaces using gradient flow with early stopping, but minimum norm interpolants are vulnerable in the overfitting regime.