5 citations · 8 across the 5 of their papers we have counts for
6 papers
Regression for matrix-valued data via Kronecker products factorization
Yin-Jen Chen, Minh Tang
We study the matrix-variate regression problem for in the high dimensional regime wherein the response are matr…
Independence testing for inhomogeneous random graphs
Yukun Song, Carey E. Priebe, Minh Tang
Testing for independence between graphs is a problem that arises naturally in social network analysis and neuroscience. In this paper, we address independence testing for inhomogen…
Adversarial contamination of networks in the setting of vertex nomination: a new trimming method
Sheyda Peyman, Minh Tang, Vince Lyzinski
As graph data becomes more ubiquitous, the need for robust inferential graph algorithms to operate in these complex data domains is crucial. In many cases of interest, inference is…
Limit theorems for eigenvectors of the normalized Laplacian for random graphs
Minh Tang, Carey E. Priebe
We prove a central limit theorem for the components of the eigenvectors corresponding to the largest eigenvalues of the normalized Laplacian matrix of a finite dimensional rand…
A semiparametric two-sample hypothesis testing problem for random dot product graphs
Minh Tang, Avanti Athreya, Daniel L. Sussman +2
Two-sample hypothesis testing for random graphs arises naturally in neuroscience, social networks, and machine learning. In this paper, we consider a semiparametric problem of two-…
Consistent adjacency-spectral partitioning for the stochastic block model when the model parameters are unknown
Donniell E. Fishkind, Daniel L. Sussman, Minh Tang +2
For random graphs distributed according to a stochastic block model, we consider the inferential task of partioning vertices into blocks using spectral techniques. Spectral partion…