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

  • Economics, Econometrics and Finance

    • Measure of Dispersion
    • Estimation Theory
    • Information

  • Mathematics

    • Bounds
    • Gaussian Process
    • Parameters
    • Bayesian
    • Number

Organisaties

Publicaties

2024
Convergence guarantees for forward gradient descent in the linear regression model, 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, 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, 1845-1877. Schmidt-Hieber, J. & Zamolodtchikov, P.https://doi.org/10.3150/23-BEJ1655Codivergences and information matrices, 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, 1510 - 1533. Derumigny, A. & Schmidt-Hieber, A. J.https://doi.org/10.1214/23-AOS2279Posterior Contraction for Deep Gaussian Process Priors, 1-49. Finocchio, G. & Schmidt-Hieber, A. J.https://jmlr.org/papers/volume24/21-0556/21-0556.pdfOn Generalization Bounds for Deep Networks Based on Loss Surface Implicit Regularization, 1203- 1223. Imaizumi, M. & Schmidt-Hieber, A. J.https://doi.org/10.1109/TIT.2022.3215088
2022
Local convergence rates of the least squares estimator with applications to transfer learning. ArXiv.org. Schmidt-Hieber, J. & Zamolodtchikov, P.https://doi.org/10.48550/arXiv.2204.05003Convergence rates of deep ReLU networks for multiclass classification, 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 unknownā€”with applications to quantitative nanoscopy, 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. University of Twente. Finocchio, G.https://doi.org/10.3990/1.9789036552356Posterior contraction for deep Gaussian process priors. Finocchio, G. & Schmidt-Hieber, A. J.https://arxiv.org/abs/2105.07410The Kolmogorovā€“Arnold representation theorem revisited, 119-126. Schmidt-Hieber, A. J.https://doi.org/10.1016/j.neunet.2021.01.020
2020
Posterior contraction rates for support boundary recovery, 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, 1432-1451. Reiss, M. & Schmidt-Hieber, A. J.https://doi.org/10.1214/19-AOS1853Rejoinder: ā€œNonparametric regression using deep neural networks with ReLU activation functionā€, 1916-1921. Schmidt-Hieber, A. J.https://doi.org/10.1214/19-AOS1931Nonparametric regression using deep neural networks with ReLU activation function, 1875-1897. Schmidt-Hieber, A. J.https://doi.org/10.1214/19-AOS1875On lower bounds for the bias-variance trade-off. ArXiv.org. Derumigny, A. & Schmidt-Hieber, J.https://arxiv.org/abs/2006.00278Ein Beitrag zur Statistischen Theorie des Deep Learnings. Verlag Dr. Hut. Langer, S.Bayesian variance estimation in the Gaussian sequence model with partial information on the means, 239-271. Finocchio, G. & Schmidt-Hieber, J.https://doi.org/10.1214/19-EJS1671

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