E-LIB Suche - Ergebnisse für: Schmidt-Hieber, Johannes

1

Convergence guarantees for forward gradient descent in the ..:

Bos, Thijs ; Schmidt-Hieber, Johannes
Journal of Statistical Planning and Inference. 233 (2024) - p. 106174 , 2024

Link: https://doi.org/10.1016/..

2

Lower bounds for the trade-off between bias and mean absolu..:

Derumigny, Alexis ; Schmidt-Hieber, Johannes
Statistics & Probability Letters. 213 (2024) - p. 110182 , 2024

Link: https://doi.org/10.1016/..

3

Johannes Schmidt-Hieber's contribution to the Discussion of..:

Schmidt-Hieber, Johannes
Journal of the Royal Statistical Society Series B: Statistical Methodology. 86 (2024) 2 - p. 329-329 , 2024

Link: https://doi.org/10.1093/..

4

Codivergences and information matrices:

Derumigny, Alexis ; Schmidt-Hieber, Johannes
Information Geometry. 7 (2024) 1 - p. 253-282 , 2024

Link: https://doi.org/10.1007/..

5

On Generalization Bounds for Deep Networks Based on Loss Su..:

Imaizumi, Masaaki ; Schmidt-Hieber, Johannes
IEEE Transactions on Information Theory. 69 (2023) 2 - p. 1203-1223 , 2023

Link: https://doi.org/10.1109/..

6

Mathematical Foundations of Machine Learning:

Bartlett, Peter L. ; Butucea, Cristina ; Schmidt-Hieber, Johannes
Oberwolfach Reports. 18 (2022) 1 - p. 853-894 , 2022

Link: https://doi.org/10.4171/..

7

The Kolmogorov–Arnold representation theorem revisited:

Schmidt-Hieber, Johannes
Neural Networks. 137 (2021) - p. 119-126 , 2021

Link: https://doi.org/10.1016/..

8

High-frequency statistics for Gaussian processes from a Le ..:

Holtz, Sebastian , 2020

Link: https://nbn-resolving.de..

9

Posterior contraction rates for support boundary recovery:

Reiß, Markus ; Schmidt-Hieber, Johannes
Stochastic Processes and their Applications. 130 (2020) 11 - p. 6638-6656 , 2020

Link: https://doi.org/10.1016/..

10

REJOINDER: "NONPARAMETRIC REGRESSION USING DEEP NEURAL NETW..:

Schmidt-Hieber, Johannes
The Annals of Statistics. 48 (2020) 4 - p. 1916-1921 , 2020

Link: https://www.jstor.org/st..

11

NONPARAMETRIC REGRESSION USING DEEP NEURAL NETWORKS WITH RE..:

Schmidt-Hieber, Johannes
The Annals of Statistics. 48 (2020) 4 - p. 1875-1897 , 2020

Link: https://www.jstor.org/st..

12

NONPARAMETRIC BAYESIAN ANALYSIS OF THE COMPOUND POISSON PRI..:

Reiss, Markus ; Schmidt-Hieber, Johannes
The Annals of Statistics. 48 (2020) 3 - p. 1432-1451 , 2020

Link: https://www.jstor.org/st..

13

A comparison of deep networks with ReLU activation function..:

Eckle, Konstantin ; Schmidt-Hieber, Johannes
Neural Networks. 110 (2019) - p. 232-242 , 2019

Link: https://doi.org/10.1016/..

14

The Le Cam distance between density estimation, Poisson pro..:

Ray, Kolyan ; Schmidt-Hieber, Johannes
Mathematical Statistics and Learning. 1 (2018) 2 - p. 101-170 , 2018

Link: https://doi.org/10.4171/..

15

A regularity class for the roots of nonnegative functions:

Ray, Kolyan ; Schmidt-Hieber, Johannes
Annali di Matematica Pura ed Applicata (1923 -). 196 (2017) 6 - p. 2091-2103 , 2017

Link: https://doi.org/10.1007/..