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math.ST2023
Convergence guarantees for forward gradient descent in the linear regression model
Thijs Bos, Johannes Schmidt-Hieber
Renewed interest in the relationship between artificial and biological neural networks motivates the study of gradient-free methods. Considering the linear regression model with ra…
math.ST2023
A supervised deep learning method for nonparametric density estimation
Thijs Bos, Johannes Schmidt-Hieber
Nonparametric density estimation is an unsupervised learning problem. In this work we propose a two-step procedure that casts the density estimation problem in the first step into…
math.ST2021
Convergence rates of deep ReLU networks for multiclass classification
Thijs Bos, Johannes Schmidt-Hieber
For classification problems, trained deep neural networks return probabilities of class memberships. In this work we study convergence of the learned probabilities to the true cond…