Nicole Mücke - RankMe

26 papers A* 1A 3Journal 22

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Random Features for Operator-Valued Kernels: Bridging Kernel Methods and Neural Operators. CoRR Mike Nguyen, Nicole Mücke
2025	J	`jnl`	Empirical risk minimization in the interpolating regime with application to neural network learning. Mach. Learn. Nicole Mücke, Ingo Steinwart
2025	J	`jnl`	Random feature approximation for general spectral methods. CoRR Mike Nguyen, Nicole Mücke
2025	J	`jnl`	Regularized least squares learning with heavy-tailed noise is minimax optimal. CoRR Mattes Mollenhauer, Nicole Mücke, Dimitri Meunier, Arthur Gretton
2024	J	`jnl`	Gradient-Based Non-Linear Inverse Learning. CoRR Abhishake, Nicole Mücke, Tapio Helin
2024	J	`jnl`	Optimal Convergence Rates for Neural Operators. CoRR Mike Nguyen, Nicole Mücke
2023	J	`jnl`	Algorithm unfolding for block-sparse and MMV problems with reduced training overhead. Frontiers Appl. Math. Stat. Jan Christian Hauffen, Peter Jung, Nicole Mücke
2023	J	`jnl`	From inexact optimization to learning via gradient concentration. Comput. Optim. Appl. Bernhard Stankewitz, Nicole Mücke, Lorenzo Rosasco
2023	J	`jnl`	How many Neurons do we need? A refined Analysis for Shallow Networks trained with Gradient Descent. CoRR Mike Nguyen, Nicole Mücke
2023	J	`jnl`	Random feature approximation for general spectral methods. CoRR Mike Nguyen, Nicole Mücke
2022	J	`jnl`	Algorithm Unfolding for Block-sparse and MMV Problems with Reduced Training Overhead. CoRR Jan Christian Hauffen, Peter Jung, Nicole Mücke
2022	A	`conf`	Data-splitting improves statistical performance in overparameterized regimes. AISTATS Nicole Mücke, Enrico Reiss, Jonas Rungenhagen, Markus Klein
2022	J	`jnl`	Local SGD in Overparameterized Linear Regression. CoRR Mike Nguyen, Charly Kirst, Nicole Mücke
2021	J	`jnl`	Data splitting improves statistical performance in overparametrized regimes. CoRR Nicole Mücke, Enrico Reiss, Jonas Rungenhagen, Markus Klein
2021	J	`jnl`	From inexact optimization to learning via gradient concentration. CoRR Bernhard Stankewitz, Nicole Mücke, Lorenzo Rosasco
2021	A	`conf`	Stochastic Gradient Descent Meets Distribution Regression. AISTATS Nicole Mücke
2020	J	`jnl`	Stochastic Gradient Descent Meets Distribution Regression. CoRR Nicole Mücke
2020	J	`jnl`	Stochastic Gradient Descent in Hilbert Scales: Smoothness, Preconditioning and Earlier Stopping. CoRR Nicole Mücke, Enrico Reiss
2019	A*	`conf`	Beating SGD Saturation with Tail-Averaging and Minibatching. NeurIPS Nicole Mücke, Gergely Neu, Lorenzo Rosasco
2019	J	`jnl`	Beating SGD Saturation with Tail-Averaging and Minibatching. CoRR Nicole Mücke, Gergely Neu, Lorenzo Rosasco
2019	J	`jnl`	Global Minima of DNNs: The Plenty Pantry. CoRR Nicole Mücke, Ingo Steinwart
2019	A	`conf`	Reducing training time by efficient localized kernel regression. AISTATS Nicole Mücke
2019	J	`jnl`	Reproducing kernel Hilbert spaces on manifolds: Sobolev and Diffusion spaces. CoRR Ernesto De Vito, Nicole Mücke, Lorenzo Rosasco
2018	J	`jnl`	Adaptivity for Regularized Kernel Methods by Lepskii's Principle. CoRR Nicole Mücke
2018	J	`jnl`	Optimal Rates for Regularization of Statistical Inverse Learning Problems. Found. Comput. Math. Gilles Blanchard, Nicole Mücke
2018	J	`jnl`	Parallelizing Spectrally Regularized Kernel Algorithms. J. Mach. Learn. Res. Nicole Mücke, Gilles Blanchard