دنبال کردن
Johannes Schmidt-Hieber
Johannes Schmidt-Hieber
University of Twente
ایمیل تأیید شده در utwente.nl
عنوان
نقل شده توسط
نقل شده توسط
سال
Nonparametric regression using deep neural networks with ReLU activation function‏
J Schmidt-Hieber‏
9762020
Bayesian linear regression with sparse priors‏
I Castillo, J Schmidt-Hieber, A Van der Vaart‏
The Annals of Statistics, 1986-2018, 2015‏
4762015
A comparison of deep networks with ReLU activation function and linear spline-type methods‏
K Eckle, J Schmidt-Hieber‏
Neural Networks 110, 232-242, 2019‏
4192019
Deep relu network approximation of functions on a manifold‏
J Schmidt-Hieber‏
arXiv preprint arXiv:1908.00695, 2019‏
1152019
The Kolmogorov–Arnold representation theorem revisited‏
J Schmidt-Hieber‏
Neural networks 137, 119-126, 2021‏
1032021
On adaptive posterior concentration rates‏
M Hoffmann, J Rousseau, J Schmidt-Hieber‏
902015
Conditions for posterior contraction in the sparse normal means problem‏
SL Van Der Pas, JB Salomond, J Schmidt-Hieber‏
702016
Multiscale methods for shape constraints in deconvolution: confidence statements for qualitative features‏
J Schmidt-Hieber, A Munk, L Dümbgen‏
582013
Convergence rates of deep ReLU networks for multiclass classification‏
T Bos, J Schmidt-Hieber‏
Electronic Journal of Statistics 16 (1), 2724-2773, 2022‏
322022
Nonparametric estimation of the volatility function in a high-frequency model corrupted by noise‏
A Munk, J Schmidt-Hieber‏
292010
Lower bounds for volatility estimation in microstructure noise models‏
A Munk, J Schmidt-Hieber‏
Borrowing Strength: Theory Powering Applications–A Festschrift for Lawrence …, 2010‏
272010
Adaptive wavelet estimation of the diffusion coefficient under additive error measurements‏
M Hoffmann, A Munk, J Schmidt-Hieber‏
Annales de l'IHP Probabilités et statistiques 48 (4), 1186-1216, 2012‏
252012
Sharp minimax estimation of the variance of Brownian motion corrupted with Gaussian noise‏
TT Cai, A Munk, J Schmidt-Hieber‏
Statistica Sinica, 1011-1024, 2010‏
202010
Tests for qualitative features in the random coefficients model‏
F Dunker, K Eckle, K Proksch, J Schmidt-Hieber‏
182019
Posterior contraction for deep Gaussian process priors‏
G Finocchio, J Schmidt-Hieber‏
Journal of Machine Learning Research 24 (66), 1-49, 2023‏
162023
Minimax theory for a class of nonlinear statistical inverse problems‏
K Ray, J Schmidt-Hieber‏
Inverse Problems 32 (6), 065003, 2016‏
162016
On the inability of Gaussian process regression to optimally learn compositional functions‏
M Giordano, K Ray, J Schmidt-Hieber‏
Advances in Neural Information Processing Systems 35, 22341-22353, 2022‏
142022
The Le Cam distance between density estimation, Poisson processes and Gaussian white noise‏
K Ray, J Schmidt-Hieber‏
Mathematical Statistics and Learning 1 (2), 101-170, 2018‏
142018
On lower bounds for the bias-variance trade-off‏
A Derumigny, J Schmidt-Hieber‏
The Annals of Statistics 51 (4), 1510-1533, 2023‏
132023
A regularity class for the roots of nonnegative functions‏
K Ray, J Schmidt-Hieber‏
Annali di Matematica Pura ed Applicata (1923-) 196, 2091-2103, 2017‏
112017
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مقاله‌ها 1–20