Institute for Stochastics and Applications (ISA)
I'm a postdoctoral researcher at University of Stuttgart, Institute of Stochastics and Applications, directed by Ingo Steinwart. I'm currently working in the field of statistical Learning Theory, in particular Deep Learning, effeciency of kernel methods, stochastic approximation methods (SGD) and regularization. My research interests also cover statistical inverse problems and adaptivity.
 Nicole Mücke, Ingo Steinwart, Global Minima of DNNs: The Plenty Pantry, https://arxiv.org/abs/1905.10686
 Nicole Mücke, Gergely Neu, Lorenzo Rosasco, Beating SGD Saturation with Tail-Averaging and Minibatching, 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada, arXiv:1902.08668
 Nicole Mücke, Reducing training time by efficient localized kernel regression
Proceedings of Machine Learning Research, PMLR 89:2603-2610, 2019.
 Nicole Mücke, Gilles Blanchard, Parallelizing Spectrally Regularized Kernel Algorithms, Journal of Machine Learning Research (2018)
 Nicole Mücke, Adaptivity for Regularized Kernel Methods by Lepskii's Principle,
 Gilles Blanchard, Nicole Mücke, Optimal Rates for Regularization of Statistical Inverse Learning Problems,
Foundations of Computational Mathematics (2017)
 Gilles Blanchard, Nicole Mücke, Kernel regression, minimax rates and effective dimensionality: beyond the regular case, to appear in Analysis and Applications (2019)
Reviewer for NeurIPS, IEEE, JMLR, COLT, Analysis and Applications