3 papers
math.ST2026
Learning of deep neural network regression estimates using gradient descent with pruning
Michael Kohler, Vincent Molinero Römer, Adam Krzyżak
Estimation of a regression function from independent and identically distributed data is considered. The error with integration with respect to the design variable is used as…
math.ST2026
Estimation of a regression function from dependent data by over-parametrized deep neural networks learned by gradient descent
Michael Kohler, Adam Krzyżak, Vincent Molinero Römer
Estimation of a regression function from exponentially -mixing data is considered. The error with integration with respect to the design is used as the error criterion. De…
math.ST2025
Statistically guided deep learning
Michael Kohler, Adam Krzyzak
We present a theoretically well-founded deep learning algorithm for nonparametric regression. It uses over-parametrized deep neural networks with logistic activation function, whic…