Author

M. Kohler

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Preprint Aug 2026

Estimation of a regression function from dependent data by over-parametrized deep neural networks learned by gradient descent

Estimation of a regression function from exponentially $\beta$-mixing data is considered. The $L_2$ error with integration with respect to the design is used as the error criterion. Deep neural network estimates with logistic activation function are defined, where all parameters are learned by gradient descent. The rat...

M. Kohler, Adam Krzyżak, Vincent Molinero Römer · 0 citations

Preprint Aug 2026

Learning of deep neural network regression estimates using gradient descent with pruning

Estimation of a regression function from independent and identically distributed data is considered. The $L_2$ error with integration with respect to the design variable is used as the error criterion. An initially randomly pruned fully connected deep neural network with logistic squasher as activation function is fitt...

M. Kohler, Vincent Molinero Römer, Adam Krzyżak · 0 citations