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stat.ML2026
Eigen-Spike Emergence and Quadratic Equivalents for Conjugate Kernels on Nonlinearly Separable Data
Collin Cranston, Zhichao Wang, Todd Kemp +1
Recent work in random matrix theory (RMT) has developed the notion of deterministic equivalents: typically linear surrogate models that approximate the spectral behavior of large n…
stat.ML2026
Free Decompression with Algebraic Spectral Curves
Siavash Ameli, Chris van der Heide, Liam Hodgkinson +1
Tools from random matrix theory have become central to deep learning theory, using spectral information to provide mechanisms for modeling generalization, robustness, scaling, and…