Adaptivity for Regularized Kernel Methods by Lepskii's Principle. - Nicole Mücke (CoRR, 2018)

Optimal Rates for Regularization of Statistical Inverse Learning Problems. - Gilles Blanchard; Nicole Mücke (Found. Comput. Math., 2018)

Parallelizing Spectrally Regularized Kernel Algorithms. - Nicole Mücke; Gilles Blanchard (J. Mach. Learn. Res., 2018)

Reducing training time by efficient localized kernel regression. - Nicole Mücke (AISTATS, 2019)

Beating SGD Saturation with Tail-Averaging and Minibatching. - Nicole Mücke; Gergely Neu; Lorenzo Rosasco (NeurIPS, 2019)

Global Minima of DNNs: The Plenty Pantry. - Nicole Mücke; Ingo Steinwart (CoRR, 2019)

Reproducing kernel Hilbert spaces on manifolds: Sobolev and Diffusion spaces. - Ernesto De Vito; Nicole Mücke; Lorenzo Rosasco (CoRR, 2019)

Stochastic Gradient Descent in Hilbert Scales: Smoothness, Preconditioning and Earlier Stopping. - Nicole Mücke; Enrico Reiss (CoRR, 2020)

Stochastic Gradient Descent Meets Distribution Regression. - Nicole Mücke (AISTATS, 2021)

Data splitting improves statistical performance in overparametrized regimes. - Nicole Mücke; Enrico Reiss; Jonas Rungenhagen; Markus Klein (CoRR, 2021)

Data-splitting improves statistical performance in overparameterized regimes. - Nicole Mücke; Enrico Reiss; Jonas Rungenhagen; Markus Klein (AISTATS, 2022)

Local SGD in Overparameterized Linear Regression. - Mike Nguyen; Charly Kirst; Nicole Mücke (CoRR, 2022)

From inexact optimization to learning via gradient concentration. - Bernhard Stankewitz; Nicole Mücke; Lorenzo Rosasco (Comput. Optim. Appl., 2023)

Random feature approximation for general spectral methods. - Mike Nguyen; Nicole Mücke (CoRR, 2023)

How many Neurons do we need? A refined Analysis for Shallow Networks trained with Gradient Descent. - Mike Nguyen; Nicole Mücke (CoRR, 2023)

Algorithm unfolding for block-sparse and MMV problems with reduced training overhead. - Jan Christian Hauffen; Peter Jung 001; Nicole Mücke (Frontiers Appl. Math. Stat., 2023)

Gradient-Based Non-Linear Inverse Learning. - Abhishake; Nicole Mücke; Tapio Helin (CoRR, 2024)

Optimal Convergence Rates for Neural Operators. - Mike Nguyen; Nicole Mücke (CoRR, 2024)

Empirical risk minimization in the interpolating regime with application to neural network learning. - Nicole Mücke; Ingo Steinwart (Mach. Learn., 2025)

Regularized least squares learning with heavy-tailed noise is minimax optimal. - Mattes Mollenhauer; Nicole Mücke; Dimitri Meunier; Arthur Gretton (CoRR, 2025)