Nicole Mücke
Nicole Mücke
Verified email at mathematik.uni-stuttgart.de - Homepage
Title
Cited by
Cited by
Year
Optimal rates for regularization of statistical inverse learning problems
G Blanchard, N Mücke
Foundations of Computational Mathematics 18 (4), 971-1013, 2018
372018
Parallelizing spectral algorithms for kernel learning
G Blanchard, N Mücke
arXiv preprint arXiv:1610.07487, 2016
162016
Optimal rates for regularization of statistical inverse learning problems
G Blanchard, N Mücke
arXiv preprint arXiv:1604.04054, 2016
142016
Parallelizing spectrally regularized kernel algorithms
N Mücke, G Blanchard
The Journal of Machine Learning Research 19 (1), 1069-1097, 2018
122018
Lepskii Principle in Supervised Learning
G Blanchard, P Mathé, N Mücke
arXiv preprint arXiv:1905.10764, 2019
32019
Kernel regression, minimax rates and effective dimensionality: Beyond the regular case
G Blanchard, N Mücke
arXiv preprint arXiv:1611.03979, 2016
32016
Global Minima of DNNs: The Plenty Pantry
N Mücke, I Steinwart
arXiv preprint arXiv:1905.10686, 2019
22019
Adaptivity for Regularized Kernel Methods by Lepskii's Principle
N Mücke
arXiv preprint arXiv:1804.05433, 2018
22018
Reducing training time by efficient localized kernel regression
N Mücke
arXiv preprint arXiv:1707.03220, 2017
12017
Direct and Inverse Problems in Machine Learning: Kernel Methods and Spectral Regularization
N Mücke
Universität Potsdam, Mathematisch-Naturwissenschaftliche Fakultät, 2017
12017
Reproducing kernel Hilbert spaces on manifolds: Sobolev and Diffusion spaces
E De Vito, N Mücke, L Rosasco
arXiv preprint arXiv:1905.10913, 2019
2019
Beating SGD Saturation with Tail-Averaging and Minibatching
N Mücke, G Neu, L Rosasco
Advances in Neural Information Processing Systems, 12568-12577, 2019
2019
LOCALNYSATION: COMBINING LOCALIZED KERNEL REGRESSION AND NYSTROM SUBSAMPLING
N MÜCKE
2017
Direct and inverse problems in machine learning
N Mücke
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