Dr. rer. nat.

Nicole Mücke

Wissenschaftliche Mitarbeiterin
Institut für Stochastik und Anwendungen
Lehrstuhl für Stochastik

Kontakt

+49 711 685-65353

Website

Pfaffenwaldring 57
70569 Stuttgart
Deutschland
Raum: 8.554

Sprechstunde

nach Vereinbarung

[3] Nicole Mücke, Reducing training time by efficient localized kernel regression
Proceedings of the 22nd International Conference on Artifcial Intelligence and Statistics (AISTATS) 2019, PMLR: Volume 89

[2] Nicole Mücke, Gilles Blanchard, Parallelizing Spectrally Regularized Kernel Algorithms, Journal of Machine Learning Research (2018)

[1] Gilles Blanchard, Nicole Mücke, Optimal Rates for Regularization of Statistical Inverse Learning Problems, Foundations of Computational Mathematics (2017) 

 

Preprints:

[9] Reproducing kernel Hilbert spaces on manifolds: Sobolev and Diffusion spaces
Ernesto De Vito, Nicole Mücke and Lorenzo Rosasco
https://arxiv.org/pdf/1905.10913.pdf

[8] Global Minima of DNNs: The lenty Pantry
Nicole Mücke and Ingo Steinwart
https://arxiv.org/pdf/1905.10686.pdf

[7] Lepskii Principle in Supervised Learning
Gilles Blanchard, Peter Mathe and Nicole Mücke
https://arxiv.org/pdf/1905.10764.pdf

[6] Nicole Mücke, Gergely Neu, Lorenzo Rosasco, Beating SGD Saturation with Tail-Averaging and Minibatching, arxiv.org/abs/1902.08668 (2019)

[5] Gilles Blanchard, Nicole Mücke, Kernel regression, minimax rates and effective dimensionality: beyond the regular case, arXiv:1611.03979v1 (2016)

[4] Nicole Mücke, Adaptivity for Regularized Kernel Methods by Lepskii's Principle,
arXiv:1804.05433v1 (2018)

 

 

 

 

 

 

Statistical Machine Learning, Efficiency of Kernel Methods, Adaptivity, Inverse Learning Problems

Please follow the link to the SWSL-Workshop, 9th-11th July 2019

https://www.nicolemuecke.com/swsl-workshop/

YES Workshop turtorial Lectures

YES X : "Understanding Deep Learning:

Generalization, Approximation and Optimization", March 19 - 22, 2019

Zum Portrait des Monats am Fachbereich, Juli 2019

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