7 citations · 19 across the 14 of their papers we have counts for
6 papers · 1 filter
All-or-Nothing Phenomena: From Single-Letter to High Dimensions
Galen Reeves, Jiaming Xu, Ilias Zadik
We consider the linear regression problem of estimating a -dimensional vector from observations , where for a real-val…
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…
Gaussian Mixture Models for Stochastic Block Models with Non-Vanishing Noise
Heather Mathews, Vaishakhi Mayya, Alexander Volfovsky +1
Community detection tasks have received a lot of attention across statistics, machine learning, and information theory with a large body of work concentrating on theoretical guaran…
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…
The All-or-Nothing Phenomenon in Sparse Linear Regression
Galen Reeves, Jiaming Xu, Ilias Zadik
We study the problem of recovering a hidden binary -sparse -dimensional vector from noisy linear observations where are i.i.d. an…