A nonasymptotic generalization error upper bound for path-regularized multilayer neural networks with Lipschitz losses that exhibits double descent and is near-minimax optimal for ReLU regression.
Nonasymptotic theory for two-layer neural networks: Beyond the bias-variance trade-off
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Path Regularization: A Near-Complete and Optimal Nonasymptotic Generalization Theory for Multilayer Neural Networks and Double Descent Phenomenon
A nonasymptotic generalization error upper bound for path-regularized multilayer neural networks with Lipschitz losses that exhibits double descent and is near-minimax optimal for ReLU regression.