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ORCIDNicole Mücke
Person information
- affiliation: MATH+ - Berlin Mathematics Research Center, Germany
- affiliation: Technical University of Berlin, Institute of Mathematics, Germany
- affiliation: University of Stuttgart, Institute for Stochastics and Applications, Germany
- affiliation: University of Potsdam, Institute of Mathematics, Germany
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2020 – today
- 2023
[j4]Bernhard Stankewitz
, Nicole Mücke, Lorenzo Rosasco:
From inexact optimization to learning via gradient concentration. Comput. Optim. Appl. 84(1): 265-294 (2023)- [j3]Jan Christian Hauffen, Peter Jung, Nicole Mücke:
Algorithm unfolding for block-sparse and MMV problems with reduced training overhead. Frontiers Appl. Math. Stat. 9 (2023) - [i12]Mike Nguyen, Nicole Mücke:
Random feature approximation for general spectral methods. CoRR abs/2308.15434 (2023) - [i11]Mike Nguyen, Nicole Mücke:
How many Neurons do we need? A refined Analysis for Shallow Networks trained with Gradient Descent. CoRR abs/2309.08044 (2023) - 2022
- [c4]Nicole Mücke, Enrico Reiss, Jonas Rungenhagen, Markus Klein:
Data-splitting improves statistical performance in overparameterized regimes. AISTATS 2022: 10322-10350 - [i10]Jan Christian Hauffen, Peter Jung, Nicole Mücke:
Algorithm Unfolding for Block-sparse and MMV Problems with Reduced Training Overhead. CoRR abs/2209.14139 (2022) - [i9]Mike Nguyen, Charly Kirst, Nicole Mücke:
Local SGD in Overparameterized Linear Regression. CoRR abs/2210.11562 (2022) - 2021
- [c3]Nicole Mücke:
Stochastic Gradient Descent Meets Distribution Regression. AISTATS 2021: 2143-2151 - [i8]Bernhard Stankewitz, Nicole Mücke, Lorenzo Rosasco:
From inexact optimization to learning via gradient concentration. CoRR abs/2106.05397 (2021) - [i7]Nicole Mücke, Enrico Reiss, Jonas Rungenhagen, Markus Klein:
Data splitting improves statistical performance in overparametrized regimes. CoRR abs/2110.10956 (2021) - 2020
- [i6]Nicole Mücke, Enrico Reiss:
Stochastic Gradient Descent in Hilbert Scales: Smoothness, Preconditioning and Earlier Stopping. CoRR abs/2006.10840 (2020) - [i5]Nicole Mücke:
Stochastic Gradient Descent Meets Distribution Regression. CoRR abs/2010.12842 (2020)
2010 – 2019
- 2019
- [c2]Nicole Mücke:
Reducing training time by efficient localized kernel regression. AISTATS 2019: 2603-2610 - [c1]Nicole Mücke, Gergely Neu, Lorenzo Rosasco:
Beating SGD Saturation with Tail-Averaging and Minibatching. NeurIPS 2019: 12568-12577 - [i4]Nicole Mücke, Gergely Neu, Lorenzo Rosasco:
Beating SGD Saturation with Tail-Averaging and Minibatching. CoRR abs/1902.08668 (2019) - [i3]Nicole Mücke, Ingo Steinwart:
Global Minima of DNNs: The Plenty Pantry. CoRR abs/1905.10686 (2019) - [i2]Ernesto De Vito, Nicole Mücke, Lorenzo Rosasco:
Reproducing kernel Hilbert spaces on manifolds: Sobolev and Diffusion spaces. CoRR abs/1905.10913 (2019) - 2018
- [j2]Gilles Blanchard, Nicole Mücke:
Optimal Rates for Regularization of Statistical Inverse Learning Problems. Found. Comput. Math. 18(4): 971-1013 (2018) - [j1]Nicole Mücke, Gilles Blanchard:
Parallelizing Spectrally Regularized Kernel Algorithms. J. Mach. Learn. Res. 19: 30:1-30:29 (2018) - [i1]Nicole Mücke:
Adaptivity for Regularized Kernel Methods by Lepskii's Principle. CoRR abs/1804.05433 (2018)
Coauthor Index
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