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stat.ML2025
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…
stat.ML2024
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…
stat.ML2018
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…