Analysis of the expected $L_2$ error of an over-parametrized deep neural network estimate learned by gradient descent without regularization

• URL: http://arxiv.org/abs/2311.14609v1
• Date: Fri, 24 Nov 2023 17:04:21 GMT
• Title: Analysis of the expected $L_2$ error of an over-parametrized deep neural network estimate learned by gradient descent without regularization
• Authors: Selina Drews and Michael Kohler
• Abstract summary: Recent results show that estimates defined by over-parametrized deep neural networks learned by applying gradient descent to a regularized empirical $L$ risk are universally consistent. In this paper, we show that the regularization term is not necessary to obtain similar results.
• Score: 7.977229957867868
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent results show that estimates defined by over-parametrized deep neural networks learned by applying gradient descent to a regularized empirical $L_2$ risk are universally consistent and achieve good rates of convergence. In this paper, we show that the regularization term is not necessary to obtain similar results. In the case of a suitably chosen initialization of the network, a suitable number of gradient descent steps, and a suitable step size we show that an estimate without a regularization term is universally consistent for bounded predictor variables. Additionally, we show that if the regression function is H\"older smooth with H\"older exponent $1/2 \leq p \leq 1$, the $L_2$ error converges to zero with a convergence rate of approximately $n^{-1/(1+d)}$. Furthermore, in case of an interaction model, where the regression function consists of a sum of H\"older smooth functions with $d^*$ components, a rate of convergence is derived which does not depend on the input dimension $d$.

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