7 citations · 19 across the 10 of their papers we have counts for
9 papers · 1 filter
Information-theoretic limits of a multiview low-rank symmetric spiked matrix model
Jean Barbier, Galen Reeves
We consider a generalization of an important class of high-dimensional inference problems, namely spiked symmetric matrix models, often used as probabilistic models for principal c…
Information-Theoretic Limits for the Matrix Tensor Product
Galen Reeves
This paper studies a high-dimensional inference problem involving the matrix tensor product of random matrices. This problem generalizes a number of contemporary data science probl…
Mutual Information in Community Detection with Covariate Information and Correlated Networks
Vaishakhi Mayya, Galen Reeves
We study the problem of community detection when there is covariate information about the node labels and one observes multiple correlated networks. We provide an asymptotic upper…
The Geometry of Community Detection via the MMSE Matrix
Galen Reeves, Vaishakhi Mayya, Alexander Volfovsky
The information-theoretic limits of community detection have been studied extensively for network models with high levels of symmetry or homogeneity. The contribution of this paper…
Understanding Phase Transitions via Mutual Information and MMSE
Galen Reeves, Henry Pfister
The ability to understand and solve high-dimensional inference problems is essential for modern data science. This article examines high-dimensional inference problems through the…
Additivity of Information in Multilayer Networks via Additive Gaussian Noise Transforms
Galen Reeves
Multilayer (or deep) networks are powerful probabilistic models based on multiple stages of a linear transform followed by a non-linear (possibly random) function. In general, the…