Zilverling

prof.dr. A.J. Schmidt-Hieber (Johannes)

Hoogleraar
EEMCS-AM-MOR
Zilverling 2057
a.j.schmidt-hieber@utwente.nl
+31534896958
Visitekaartje

Johannes Schmidt-Hieber is hoogleraar statistiek op deĀ Universiteit Twente. Link naar persoonlijke webpagina

Expertises

  • Mathematics

    • Gaussian Process
    • Bounds
    • Kriging
    • Parameters
    • Bayesian

  • Economics, Econometrics and Finance

    • Measure of Dispersion
    • Estimation Theory
    • Information

Organisaties

Publicaties

Jump to: 2025 | 2024 | 2023 | 2022 | 2021

2025

Transfer learning, generative modelling, and nonparametric regression (2025)[Thesis › PhD Thesis - Research UT, graduation UT]. University of Twente. Zamolodtchikov, P.https://doi.org/10.3990/1.9789036564649

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.2024.106174Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling (2024)[Working paper › Preprint]. ArXiv.org. Dexheimer, N. & Schmidt-Hieber, J.https://doi.org/10.48550/arXiv.2411.17567Understanding the Effect of GCN Convolutions in Regression Tasks (2024)[Working paper › Preprint]. ArXiv.org. Chen, J., Schmidt-Hieber, J., Donnat, C. & Klopp, O.https://doi.org/10.48550/arXiv.2410.20068On the VC dimension of deep group convolutional neural networks (2024)[Working paper › Preprint]. ArXiv.org. Sepliarskaia, A., Langer, S. & Schmidt-Hieber, J.https://doi.org/10.48550/arXiv.2410.15800Lower 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-BEJ1655Dropout Regularization Versus l2-Penalization in the Linear Model (2024)Journal of machine learning research, 25, 1-48. Article 204. Clara, G., Langer, S. & Schmidt-Hieber, J.https://www.jmlr.org/papers/v25/23-0803.htmlCodivergences and information matrices (2024)Information Geometry, 7, 253-282. Derumigny, A. & Schmidt-Hieber, J.https://doi.org/10.1007/s41884-024-00135-2On the inability of Gaussian process regression to optimally learn compositional functions (2024)In NIPS'22: Proceedings of the 36th International Conference on Neural Information Processing Systems (pp. 22341 -22353). Article 1623. Curran Associates Inc.. Giordano, M., Ray, K. & Schmidt-Hieber, J.https://doi.org/10.5555/3600270Johannes Schmidt-Hieber's contribution to the Discussion of 'the Discussion Meeting on Probabilistic and statistical aspects of machine learning' (2024)Journal of the Royal Statistical Society. Series B: Statistical Methodology, 86(2), 329. Schmidt-Hieber, J.https://doi.org/10.1093/jrsssb/qkae007Correction to ā€œNonparametric regression using deep neural networks with ReLU activation functionā€ (2024)Annals of statistics, 52(1), 413-414. Schmidt-Hieber, J. & Vu, D.https://doi.org/10.1214/24-AOS2351A supervised deep learning method for nonparametric density estimation (2024)Electronic Journal of Statistics, 18(2), 5601-5658. Bos, T. & Schmidt-Hieber, J.https://doi.org/10.1214/24-EJS2332

2023

Hebbian learning inspired estimation of the linear regression parameters from queries (2023)[Working paper › Preprint]. ArXiv.org. Schmidt-Hieber, A. J. & Koolen, W. M.https://doi.org/10.48550/arXiv.2311.03483On 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-AOS2279Lower bounds for the trade-off between bias and mean absolute deviation (2023)[Working paper › Preprint]. ArXiv.org. Derumigny, A. & Schmidt-Hieber, J.https://doi.org/10.48550/arXiv.2303.11706On 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

On the inability of Gaussian process regression to optimally learn compositional functions (2022)[Working paper › Preprint]. ArXiv.org. Giordano, M., Ray, K. & Schmidt-Hieber, J.https://doi.org/10.48550/arXiv.2205.07764Local 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-EJS2011On the inability of Gaussian process regression to optimally learn compositional functions (2022)In 36th Conference on Neural Information Processing Systems, NeurIPS 2022 (Advances in Neural Information Processing Systems; Vol. 35). Neural information processing systems foundation. Giordano, M., Ray, K. & Schmidt-Hieber, J.https://papers.nips.cc/paper_files/paper/2022/hash/8c420176b45e923cf99dee1d7356a763-Abstract-Conference.html

2021

Posterior analysis of n in the binomial (n,p) problem with both parameters unknownā€”with 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/2105.07410

Onderzoeksprofielen

Scan de QR-code of
Download vCard