This paper studies the problem of matching two complete graphs with edge weights
correlated through latent geometries, extending a recent line of research on random graph …

We study the problem of efficiently recovering the matching between an unlabelled
collection of n points in ℝ d and a small random perturbation of those points. We consider a …

Graph matching, also known as network alignment, refers to finding a bijection between the
vertex sets of two given graphs so as to maximally align their edges. This fundamental …

We study the problem of reconstructing a perfect matching M∗ hidden in a randomly
weighted n× n bipartite graph. The edge set includes every node pair in M∗ and each of the …

We consider the statistical inference problem of recovering an unknown perfect matching,
hidden in a weighted random graph, by exploiting the information arising from the use of two …

This paper considers the problem of detecting whether two databases, each consisting of n
users with d Gaussian features, are correlated. Under the null hypothesis, the databases are …

We study the problem of detecting the correlation between two Gaussian databases
$\mathsf {X}\in\mathbb {R}^{n\times d} $ and $\mathsf {Y}^{n\times d} $, each composed of …

We connect the study of phase transitions in high-dimensional statistical inference to the
study of threshold phenomena in random graphs. A major question in the study of the Erdős …

We study when low coordinate degree functions (LCDF)--linear combinations of functions
depending on small subsets of entries of a vector--can hypothesis test between high …

We consider the problem of recovering an unknown k-factor, hidden in a weighted random
graph. For k= 1 this is the planted matching problem, while the k= 2 case is closely related to …