3 papers
stat.ML2026
Power-Law Spectrum of the Random Feature Model
Elliot Paquette, Ke Liang Xiao, Yizhe Zhu
Scaling laws for neural networks, in which the loss decays as a power-law in the number of parameters, data, and compute, depend fundamentally on the spectral structure of the data…
math.PR2025
A semicircle law for the normalized Laplacian of sparse random graphs
Yiming Chen, Zijun Chen, Yizhe Zhu
We study the limiting spectral distribution of the normalized Laplacian of an ErdÅs-Rényi graph . To account for the presence of isolated vertices in the spa…
math.PR2025
Central limit theorems for linear spectral statistics of inhomogeneous random graphs with graphon limits
Xiangyi Zhu, Yizhe Zhu
We establish central limit theorems (CLTs) for the linear spectral statistics of the adjacency matrix of inhomogeneous random graphs across all sparsity regimes, providing explicit…