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Nicole Mücke
Nicole Mücke
Technical University Brunswick
Verified email at tu-braunschweig.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
882018
Parallelizing spectrally regularized kernel algorithms
N Mücke, G Blanchard
The Journal of Machine Learning Research 19 (1), 1069-1097, 2018
372018
Beating SGD saturation with tail-averaging and minibatching
N Mücke, G Neu, L Rosasco
Advances in Neural Information Processing Systems 32, 2019
222019
Optimal rates for regularization of statistical inverse learning problems
G Blanchard, N Mücke
arXiv preprint arXiv:1604.04054, 2016
162016
Parallelizing spectral algorithms for kernel learning
G Blanchard, N Mücke
arXiv preprint arXiv:1610.07487, 2016
152016
Reproducing kernel Hilbert spaces on manifolds: Sobolev and Diffusion spaces
E De Vito, N Mücke, L Rosasco
Analysis and Applications 19 (03), 363-396, 2021
122021
Global minima of DNNs: The plenty pantry
N Mücke, I Steinwart
arXiv preprint arXiv:1905.10686, 169, 2019
112019
Lepskii principle in supervised learning
G Blanchard, P Mathé, N Mücke
arXiv preprint arXiv:1905.10764, 2019
72019
Reducing training time by efficient localized kernel regression
N Müecke
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
72019
Kernel regression, minimax rates and effective dimensionality: Beyond the regular case
G Blanchard, N Mücke
Analysis and Applications 18 (04), 683-696, 2020
62020
Stochastic gradient descent meets distribution regression
N Mücke
International Conference on Artificial Intelligence and Statistics, 2143-2151, 2021
52021
From inexact optimization to learning via gradient concentration
B Stankewitz, N Mücke, L Rosasco
Computational Optimization and Applications, 1-30, 2022
32022
Stochastic gradient descent in Hilbert scales: smoothness, preconditioning and earlier stopping
N Mücke, E Reiss
arXiv preprint arXiv:2006.10840, 2020
32020
Adaptivity for Regularized Kernel Methods by Lepskii's Principle
N Mücke
arXiv preprint arXiv:1804.05433, 2018
22018
Direct and inverse problems in machine learning: kernel methods and spectral regularization
N Mücke
Universität Potsdam, 2017
22017
Direct and inverse problems in machine learning: kernel methods and spectral regularization
N Mücke
Universität Potsdam, 2017
22017
Kernel regression, minimax rates and effective dimensionality: Beyond the regular case
G Blanchard, N Mücke
arXiv preprint arXiv:1611.03979, 2016
22016
Data-splitting improves statistical performance in overparameterized regimes
N Mücke, E Reiss, J Rungenhagen, M Klein
International Conference on Artificial Intelligence and Statistics, 10322-10350, 2022
12022
Empirical Risk Minimization in the Interpolating Regime with Application to Neural Network Learning
N Mücke, I Steinwart
arXiv preprint arXiv:1905.10686, 2019
12019
LOCALNYSATION: COMBINING LOCALIZED KERNEL REGRESSION AND NYSTROM SUBSAMPLING
N MÜCKE
2017
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