21 citations · 47 across the 5 of their papers we have counts for
11 papers
Empirical Bayes PCA in high dimensions
Xinyi Zhong, Chang Su, Zhou Fan
When the dimension of data is comparable to or larger than the number of data samples, Principal Components Analysis (PCA) may exhibit problematic high-dimensional noise. In this w…
Tree-Projected Gradient Descent for Estimating Gradient-Sparse Parameters on Graphs
Sheng Xu, Zhou Fan, Sahand Negahban
We study estimation of a gradient-sparse parameter vector , having strong gradient-sparsity on an underlying g…
Spectra of the Conjugate Kernel and Neural Tangent Kernel for linear-width neural networks
Zhou Fan, Zhichao Wang
We study the eigenvalue distributions of the Conjugate Kernel and Neural Tangent Kernel associated to multi-layer feedforward neural networks. In an asymptotic regime where network…
Likelihood landscape and maximum likelihood estimation for the discrete orbit recovery model
Zhou Fan, Yi Sun, Tianhao Wang +1
We study the non-convex optimization landscape for maximum likelihood estimation in the discrete orbit recovery model with Gaussian noise. This model is motivated by applications i…
Spectral Graph Matching and Regularized Quadratic Relaxations II: Erdős-Rényi Graphs and Universality
Zhou Fan, Cheng Mao, Yihong Wu +1
We analyze a new spectral graph matching algorithm, GRAph Matching by Pairwise eigen-Alignments (GRAMPA), for recovering the latent vertex correspondence between two unlabeled, edg…
Spectral Graph Matching and Regularized Quadratic Relaxations I: The Gaussian Model
Zhou Fan, Cheng Mao, Yihong Wu +1
Graph matching aims at finding the vertex correspondence between two unlabeled graphs that maximizes the total edge weight correlation. This amounts to solving a computationally in…