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Nicole Mücke

Affiliations:
  • MATH+ - Berlin Mathematics Research Center, Germany
  • Technical University of Berlin, Institute of Mathematics, Germany
  • University of Stuttgart, Institute for Stochastics and Applications, Germany
  • University of Potsdam, Institute of Mathematics, Germany


According to our database1, Nicole Mücke authored at least 16 papers between 2018 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Online presence:

On csauthors.net:

Bibliography

2023
Algorithm unfolding for block-sparse and MMV problems with reduced training overhead.
[BibTeX]
[DOI]
Jan Christian Hauffen
,
Peter Jung
,
Nicole Mücke
Frontiers Appl. Math. Stat., 2023

How many Neurons do we need? A refined Analysis for Shallow Networks trained with Gradient Descent.
[BibTeX]
[DOI]
Mike Nguyen
,
Nicole Mücke
CoRR, 2023

Random feature approximation for general spectral methods.
[BibTeX]
[DOI]
Mike Nguyen
,
Nicole Mücke
CoRR, 2023

From inexact optimization to learning via gradient concentration.
[BibTeX]
[DOI]
Bernhard Stankewitz
,
Nicole Mücke
,
Lorenzo Rosasco
Comput. Optim. Appl., 2023

2022
Local SGD in Overparameterized Linear Regression.
[BibTeX]
[DOI]
Mike Nguyen
,
Charly Kirst
,
Nicole Mücke
CoRR, 2022

Data-splitting improves statistical performance in overparameterized regimes.
[BibTeX]
[DOI]
Nicole Mücke
,
Enrico Reiss
,
Jonas Rungenhagen
,
Markus Klein
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Data splitting improves statistical performance in overparametrized regimes.
[BibTeX]
[DOI]
Nicole Mücke
,
Enrico Reiss
,
Jonas Rungenhagen
,
Markus Klein
CoRR, 2021

Stochastic Gradient Descent Meets Distribution Regression.
[BibTeX]
[DOI]
Nicole Mücke
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Stochastic Gradient Descent in Hilbert Scales: Smoothness, Preconditioning and Earlier Stopping.
[BibTeX]
[DOI]
Nicole Mücke
,
Enrico Reiss
CoRR, 2020

2019
Reproducing kernel Hilbert spaces on manifolds: Sobolev and Diffusion spaces.
[BibTeX]
[DOI]
Ernesto De Vito
,
Nicole Mücke
,
Lorenzo Rosasco
CoRR, 2019

Global Minima of DNNs: The Plenty Pantry.
[BibTeX]
[DOI]
Nicole Mücke
,
Ingo Steinwart
CoRR, 2019

Beating SGD Saturation with Tail-Averaging and Minibatching.
[BibTeX]
[DOI]
Nicole Mücke
,
Gergely Neu
,
Lorenzo Rosasco
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Reducing training time by efficient localized kernel regression.
[BibTeX]
[DOI]
Nicole Mücke
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018
Parallelizing Spectrally Regularized Kernel Algorithms.
[BibTeX]
[DOI]
Nicole Mücke
,
Gilles Blanchard
J. Mach. Learn. Res., 2018

Optimal Rates for Regularization of Statistical Inverse Learning Problems.
[BibTeX]
[DOI]
Gilles Blanchard
,
Nicole Mücke
Found. Comput. Math., 2018

Adaptivity for Regularized Kernel Methods by Lepskii's Principle.
[BibTeX]
[DOI]
Nicole Mücke
CoRR, 2018


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