8 citations · 26 across the 13 of their papers we have counts for
Showing 2026Show all
3 papers · 1 filter
stat.ML2026
Brownian Heads for Deep ReLU Representations: Activation Mass and the Cost of Same-Sample Selection
Mahdi Mohammadigohari, Nicole Mücke
Deep representation learning often selects hidden features and fits the final predictor on the same sample, so fixed-feature analysis performed after selection can omit selection c…
stat.ML2026
A Smoothed Discrepancy Principle for Random Feature Methods and Neural Networks
Mike Nguyen, Nicole Mücke
We study data-driven early stopping for spectral regularisation methods in the classical non-parametric regression setting. Building on the discrepancy principle, we propose a mult…
stat.ML2026
Random Features for Operator-Valued Kernels: Bridging Kernel Methods and Neural Operators
Mike Nguyen, Nicole Mücke
In this work, we investigate the generalization properties of random feature methods. Our analysis extends prior results for Tikhonov regularization to a broad class of spectral re…