Takip et
Johannes Schmidt-Hieber
Johannes Schmidt-Hieber
University of Twente
utwente.nl üzerinde doğrulanmış e-posta adresine sahip
Başlık
Alıntı yapanlar
Alıntı yapanlar
Yıl
Nonparametric regression using deep neural networks with ReLU activation function
J Schmidt-Hieber
10272020
Bayesian linear regression with sparse priors
I Castillo, J Schmidt-Hieber, A Van der Vaart
The Annals of Statistics, 1986-2018, 2015
4862015
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
4352019
The Kolmogorov–Arnold representation theorem revisited
J Schmidt-Hieber
Neural networks 137, 119-126, 2021
1432021
Deep relu network approximation of functions on a manifold
J Schmidt-Hieber
arXiv preprint arXiv:1908.00695, 2019
1212019
On adaptive posterior concentration rates
M Hoffmann, J Rousseau, J Schmidt-Hieber
942015
Conditions for posterior contraction in the sparse normal means problem
SL Van Der Pas, JB Salomond, J Schmidt-Hieber
722016
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
332022
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
172023
Minimax theory for a class of nonlinear statistical inverse problems
K Ray, J Schmidt-Hieber
Inverse Problems 32 (6), 065003, 2016
162016
On lower bounds for the bias-variance trade-off
A Derumigny, J Schmidt-Hieber
The Annals of Statistics 51 (4), 1510-1533, 2023
152023
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
152022
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
152018
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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