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: 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/s41884-024-00135-2On 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/qkae007

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-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/2105.07410The Kolmogorovā€“Arnold 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: ā€œNonparametric 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

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