Preprint news

October 7, 2020 /

Adaptive Learning Rates for Support Vector Machines Working on Data with Low Intrinsic Dimension


László Györfi, Harro Walk, Nearest neighbor based conformal prediction, 2020. (Verfügbar unter) Stuttgarter Mathematische Berichte 2020-002.

Thomas Berrett, László Györfi, Harro Walk, Strongly universally consistent nonparametric regression and classification with privatised data, 2020

David Holzmüller, On the Universality of the Double Descent Peak in Ridgeless Regression, 2020

Daniel F. B. Haeufle, Isabell Wochner, David Holzmüller, Danny Driess, Michael Günther, Syn Schmitt, Muscles Reduce Neuronal Information Load: Quantification of Control Effort in Biological vs. Robotic Pointing and Walking, 2020

Thomas Hamm, Ingo Steinwart, Adaptive Learning Rates for Support Vector Machines Working on Data with Low Intrinsic Dimension, 2020

Ingo Steinwart, Reproducing Kernel Hilbert Spaces Cannot Contain all Continuous Functions on a Compact Metric Space, 2020

David Holzmüller, Ingo Steinwart, Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent, 2020

Simon Fischer, Some new bounds on the entropy numbers of diagonal operators, J. Approx. Theory, 2020.


Alina Braun, Michael Kohler, Harro Walk, On the rate of convergence of a neural network regression estimate learned by gradient descent, 2019. (Verfügbar unter) Stuttgarter Mathematische Berichte 2019-003.

Ingo Steinwart, Simon Fischer, A closer look at covering number bounds for Gaussian kernels, 2019, accepted in: Journal of Complexity

Simon Fischer and Ingo Steinwart, Sobolev norm learning rates for regularized least-squares algorithms, arXiv e-prints, 2019 accepted in: Journal of Machine Learning Research

Uta Freiberg und Stefan Kohl, Martin boundary theory on inhomogenous fractals, 2019

Ernesto De Vito, Nicole Mücke and Lorenzo Rosasco, Reproducing kernel Hilbert spaces on manifolds: Sobolev and Diffusion spaces

Nicole Mücke and Ingo Steinwart, Global Minima of DNNs: The lenty Pantry

Gilles Blanchard, Peter Mathe and Nicole Mücke, Lepskii Principle in Supervised Learning

I. Blaschzyk, I. Steinwart, Improved Classification Rates for Localized SVMs, arXiv:1905.01502 (2019) . [ preprint.pdf ]

I. Steinwart, A sober look at neural network initializations, tech. rep., Fakultät für Mathematik und Physik, Universität Stuttgart, 2019. [ preprint.pdf ]

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