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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
1312018
Parallelizing spectrally regularized kernel algorithms
N MÞcke, G Blanchard
Journal of Machine Learning Research 19 (30), 1-29, 2018
522018
Beating SGD saturation with tail-averaging and minibatching
N Mücke, G Neu, L Rosasco
Advances in Neural Information Processing Systems 32, 2019
392019
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
262021
Parallelizing spectral algorithms for kernel learning
G Blanchard, N Mücke
arXiv preprint arXiv:1610.07487, 2016
182016
Optimal rates for regularization of statistical inverse learning problems
G Blanchard, N Mücke
arXiv preprint arXiv:1604.04054, 2016
162016
Learning linear operators: Infinite-dimensional regression as a well-behaved non-compact inverse problem
M Mollenhauer, N Mücke, TJ Sullivan
arXiv preprint arXiv:2211.08875, 2022
142022
Reducing training time by efficient localized kernel regression
N Müecke
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
132019
Global minima of DNNs: The plenty pantry
N Mücke, I Steinwart
arXiv preprint arXiv:1905.10686, 169, 2019
122019
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
112022
Lepskii principle in supervised learning
G Blanchard, P Mathé, N Mücke
arXiv preprint arXiv:1905.10764, 2019
112019
Stochastic gradient descent meets distribution regression
N Mücke
International Conference on Artificial Intelligence and Statistics, 2143-2151, 2021
82021
From inexact optimization to learning via gradient concentration
B Stankewitz, N Mücke, L Rosasco
Computational Optimization and Applications 84 (1), 265-294, 2023
72023
Kernel regression, minimax rates and effective dimensionality: Beyond the regular case
G Blanchard, N Mücke
Analysis and Applications 18 (04), 683-696, 2020
72020
Stochastic gradient descent in Hilbert scales: Smoothness, preconditioning and earlier stopping
N Mücke, E Reiss
arXiv preprint arXiv:2006.10840, 2020
72020
Adaptivity for Regularized Kernel Methods by Lepskii's Principle
N Mücke
arXiv preprint arXiv:1804.05433, 2018
32018
Empirical Risk Minimization in the Interpolating Regime with Application to Neural Network Learning
N Mücke, I Steinwart
arXiv preprint arXiv:1905.10686, 2019
22019
Kernel regression, minimax rates and effective dimensionality: Beyond the regular case
G Blanchard, N Mücke
arXiv preprint arXiv:1611.03979, 2016
22016
Random feature approximation for general spectral methods
M Nguyen, N Mücke
arXiv preprint arXiv:2308.15434, 2023
12023
Direct and inverse problems in machine learning: kernel methods and spectral regularization
N Mücke
Universität Potsdam, 2017
12017
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