Nicole Mücke

Publication Activity (10 Years)

Years Active: 2018-2023
Publications (10 Years): 20

Top Topics

Stochastic Gradient Descent

Top Venues

Found. Comput. Math.

J. Mach. Learn. Res.

Publications

Mike Nguyen, Nicole Mücke
Random feature approximation for general spectral methods. CoRR (2023)
Mike Nguyen, Nicole Mücke
How many Neurons do we need? A refined Analysis for Shallow Networks trained with Gradient Descent. CoRR (2023)
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)
Bernhard Stankewitz, Nicole Mücke, Lorenzo Rosasco
From inexact optimization to learning via gradient concentration. Comput. Optim. Appl. 84 (1) (2023)
Nicole Mücke, Enrico Reiss, Jonas Rungenhagen, Markus Klein
Data-splitting improves statistical performance in overparameterized regimes. AISTATS (2022)
Jan Christian Hauffen, Peter Jung, Nicole Mücke
Algorithm Unfolding for Block-sparse and MMV Problems with Reduced Training Overhead. CoRR (2022)
Mike Nguyen, Charly Kirst, Nicole Mücke
Local SGD in Overparameterized Linear Regression. CoRR (2022)
Nicole Mücke
Stochastic Gradient Descent Meets Distribution Regression. AISTATS (2021)
Nicole Mücke, Enrico Reiss, Jonas Rungenhagen, Markus Klein
Data splitting improves statistical performance in overparametrized regimes. CoRR (2021)
Bernhard Stankewitz, Nicole Mücke, Lorenzo Rosasco
From inexact optimization to learning via gradient concentration. CoRR (2021)
Nicole Mücke
Stochastic Gradient Descent Meets Distribution Regression. CoRR (2020)
Nicole Mücke, Enrico Reiss
Stochastic Gradient Descent in Hilbert Scales: Smoothness, Preconditioning and Earlier Stopping. CoRR (2020)
Ernesto De Vito, Nicole Mücke, Lorenzo Rosasco
Reproducing kernel Hilbert spaces on manifolds: Sobolev and Diffusion spaces. CoRR (2019)
Nicole Mücke, Gergely Neu, Lorenzo Rosasco
Beating SGD Saturation with Tail-Averaging and Minibatching. CoRR (2019)
Nicole Mücke, Ingo Steinwart
Global Minima of DNNs: The Plenty Pantry. CoRR (2019)
Nicole Mücke
Reducing training time by efficient localized kernel regression. AISTATS (2019)
Nicole Mücke, Gergely Neu, Lorenzo Rosasco
Beating SGD Saturation with Tail-Averaging and Minibatching. NeurIPS (2019)
Gilles Blanchard, Nicole Mücke
Optimal Rates for Regularization of Statistical Inverse Learning Problems. Found. Comput. Math. 18 (4) (2018)
Nicole Mücke
Adaptivity for Regularized Kernel Methods by Lepskii's Principle. CoRR (2018)
Nicole Mücke, Gilles Blanchard
Parallelizing Spectrally Regularized Kernel Algorithms. J. Mach. Learn. Res. 19 (2018)