Johannes Schmidt-Hieber is hoogleraar statistiek op deĀ Universiteit Twente. Link naar persoonlijke webpagina
Expertises
Economics, Econometrics and Finance
- Measure of Dispersion
- Estimation Theory
- Information
Mathematics
- Bounds
- Gaussian Process
- Parameters
- Bayesian
- Number
Organisaties
Publicaties
Jump to: 2024 | 2023 | 2022 | 2021 | 2020
2024
Convergence guarantees for forward gradient descent in the linear regression model (2024)Journal of statistical planning and inference, 233. Article 106174. Bos, T. & Schmidt-Hieber, J.https://doi.org/10.1016/j.jspi.2024.106174Lower bounds for the trade-off between bias and mean absolute deviation (2024)Statistics & probability letters, 213. Article 110182. Derumigny, A. & Schmidt-Hieber, J.https://doi.org/10.1016/j.spl.2024.110182Local convergence rates of the nonparametric least squares estimator with applications to transfer learning (2024)Bernoulli, 30(3), 1845-1877. Schmidt-Hieber, J. & Zamolodtchikov, P.https://doi.org/10.3150/23-BEJ1655Codivergences and information matrices (2024)Information Geometry, 7, 253-282. Derumigny, A. & Schmidt-Hieber, J.https://doi.org/10.1007/s41884-024-00135-2
2023
On lower bounds for the bias-variance trade-off (2023)Annals of the Institute of Statistical Mathematics, 51(4), 1510 - 1533. Derumigny, A. & Schmidt-Hieber, A. J.https://doi.org/10.1214/23-AOS2279On Generalization Bounds for Deep Networks Based on Loss Surface Implicit Regularization (2023)IEEE transactions on information theory, 69(2), 1203- 1223. Imaizumi, M. & Schmidt-Hieber, A. J.https://doi.org/10.1109/TIT.2022.3215088Posterior Contraction for Deep Gaussian Process Priors (2023)Journal of machine learning research, 24(66), 1-49. Finocchio, G. & Schmidt-Hieber, A. J.https://jmlr.org/papers/volume24/21-0556/21-0556.pdf
2022
Local convergence rates of the least squares estimator with applications to transfer learning (2022)[Working paper › Preprint]. ArXiv.org. Schmidt-Hieber, J. & Zamolodtchikov, P.https://doi.org/10.48550/arXiv.2204.05003Convergence rates of deep ReLU networks for multiclass classification (2022)Electronic Journal of Statistics, 16(1), 2724 - 2773. Bos, T. & Schmidt-Hieber, A. J.https://doi.org/10.1214/22-EJS2011
2021
Posterior analysis of n in the binomial (n,p) problem with both parameters unknownāwith applications to quantitative nanoscopy (2021)Annals of statistics, 49(6), 3534-3558. Schmidt-Hieber, A. J., Schneider, L., Staudt, T., Krajina, A., Aspelmeier, T. & Munk, A.https://doi.org/10.1214/21-AOS2096Two perspectives on high-dimensional estimation problems: posterior contraction and median-of-means (2021)[Thesis › PhD Thesis - Research UT, graduation UT]. University of Twente. Finocchio, G.https://doi.org/10.3990/1.9789036552356Posterior contraction for deep Gaussian process priors (2021)[Working paper › Working paper]. Finocchio, G. & Schmidt-Hieber, A. J.https://arxiv.org/abs/2105.07410The KolmogorovāArnold representation theorem revisited (2021)Neural networks, 137, 119-126. Schmidt-Hieber, A. J.https://doi.org/10.1016/j.neunet.2021.01.020
2020
Posterior contraction rates for support boundary recovery (2020)Stochastic processes and their applications, 130(11), 6638. Reiss, M. & Schmidt-Hieber, J.https://doi.org/10.1016/j.spa.2020.06.005Nonparametric Bayesian analysis of the compound Poisson prior for support boundary recovery (2020)Annals of statistics, 48(3), 1432-1451. Reiss, M. & Schmidt-Hieber, A. J.https://doi.org/10.1214/19-AOS1853Nonparametric regression using deep neural networks with ReLU activation function (2020)Annals of statistics, 48(4), 1875-1897. Schmidt-Hieber, A. J.https://doi.org/10.1214/19-AOS1875Rejoinder: āNonparametric regression using deep neural networks with ReLU activation functionā (2020)Annals of statistics, 48(4), 1916-1921. Schmidt-Hieber, A. J.https://doi.org/10.1214/19-AOS1931On lower bounds for the bias-variance trade-off (2020)[Working paper › Working paper]. ArXiv.org. Derumigny, A. & Schmidt-Hieber, J.https://arxiv.org/abs/2006.00278Ein Beitrag zur Statistischen Theorie des Deep Learnings (2020)[Thesis › PhD Thesis - Research external, graduation external]. Verlag Dr. Hut. Langer, S.Bayesian variance estimation in the Gaussian sequence model with partial information on the means (2020)Electronic Journal of Statistics, 14(1), 239-271. Finocchio, G. & Schmidt-Hieber, J.https://doi.org/10.1214/19-EJS1671
Onderzoeksprofielen
Vakken collegejaar 2024/2025
Vakken in het huidig collegejaar worden toegevoegd op het moment dat zij definitief zijn in het Osiris systeem. Daarom kan het zijn dat de lijst nog niet compleet is voor het gehele collegejaar.
- 191508209 - Internship AM
- 191508309 - Final Project (combination)
- 191508409 - Final Project M-AM
- 202001350 - Analysis II
- 202300016 - Mathematical Statistics 1
- 202300017 - Analysis 3
- 202300018 - Reflection 1
- 202300026 - Mathematical Statistics 2
- 202300130 - Capita Selecta Applied Mathematics
- 202400606 - Statistical Learning
Vakken collegejaar 2023/2024
- 191508209 - Internship AM
- 191508309 - Final Project (combination)
- 191508409 - Final Project M-AM
- 200900030 - Onderzoek van Wiskunde
- 201900115 - Statistical Learning
- 202001348 - Mathematical Statistics
- 202001349 - Project Statistics
- 202001350 - Analysis II
- 202001351 - Prooflab Revisited: Diversity in Culture
- 202001385 - Bachelor Assignment AM-TCS Double Degree
- 202100112 - Graphical Models and Causality
- 202200398 - Internship AM-CS
- 202300016 - Mathematical Statistics 1
- 202300017 - Analysis 3
- 202300018 - Reflection 1
- 202300026 - Mathematical Statistics 2
- 202300130 - Capita Selecta Applied Mathematics
Adres
![](/.uc/iff4689c40103f3eb1100f2c8f403e85637f06a7b284b0801e3bc0268018041/zilverling.jpg)
Universiteit Twente
Zilverling (gebouwnr. 11), kamer 2057
Hallenweg 19
7522 NH Enschede
Universiteit Twente
Zilverling 2057
Postbus 217
7500 AE Enschede