1 citations · 2 across the 3 of their papers we have counts for
5 papers
Random feature approximation for general spectral methods
Mike Nguyen, Nicole Mücke
Random feature approximation is arguably one of the most widely used techniques for kernel methods in large-scale learning algorithms. In this work, we analyze the generalization p…
Regularized least squares learning with heavy-tailed noise is minimax optimal
Mattes Mollenhauer, Nicole Mücke, Dimitri Meunier +1
This paper examines the performance of ridge regression in reproducing kernel Hilbert spaces in the presence of noise that exhibits a finite number of higher moments. We establish…
Gradient-Based Non-Linear Inverse Learning
Abhishake, Nicole Mücke, Tapio Helin
We study statistical inverse learning in the context of nonlinear inverse problems under random design. Specifically, we address a class of nonlinear problems by employing gradient…
Random feature approximation for general spectral methods
Mike Nguyen, Nicole Mücke
Random feature approximation is arguably one of the most popular techniques to speed up kernel methods in large scale algorithms and provides a theoretical approach to the analysis…
Adaptivity for Regularized Kernel Methods by Lepskii's Principle
Nicole Mücke
We address the problem of {\it adaptivity} in the framework of reproducing kernel Hilbert space (RKHS) regression. More precisely, we analyze estimators arising from a linear regul…