48 citations · 175 across the 26 of their papers we have counts for
21 papers · 1 filter
Lossy Compression via Sparse Regression Codes: Generalized Construction and Finite-length Bounds
Galen Reeves, Ramji Venkataramanan
We study sparse regression codes (SPARCs) for lossy compression under simple greedy encoding rules, including both correlation-based and distance-based methods. We generalize the S…
From Symmetry to Capacity: Nested Codes on Binary Memoryless Symmetric Channels
Henry D. Pfister, Galen Reeves
The past decade has seen notable advances in our understanding of structured error-correcting codes, particularly binary Reed-Muller (RM) codes. While initial breakthroughs were fo…
Achieving Capacity on Non-Binary Channels with Generalized Reed-Muller Codes
Galen Reeves, Henry D. Pfister
Recently, the authors showed that Reed-Muller (RM) codes achieve capacity on binary memoryless symmetric (BMS) channels with respect to bit error rate. This paper extends that work…
Reed-Muller Codes on BMS Channels Achieve Vanishing Bit-Error Probability for All Rates Below Capacity
Galen Reeves, Henry D. Pfister
This paper considers the performance of Reed-Muller (RM) codes transmitted over binary memoryless symmetric (BMS) channels under bitwise maximum-a-posteriori (bit-MAP) decoding. It…
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…
Gaussian Approximation of Quantization Error for Estimation from Compressed Data
Alon Kipnis, Galen Reeves
We consider the distributional connection between the lossy compressed representation of a high-dimensional signal using a random spherical code and the observation of unde…
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…
Two-Moment Inequalities for Rényi Entropy and Mutual Information
Galen Reeves
This paper explores some applications of a two-moment inequality for the integral of the -th power of a function, where . The first contribution is an upper bound on t…
Information-Theoretic Analysis of Refractory Effects in the P300 Speller
Vaishakhi Mayya, Boyla Mainsah, Galen Reeves
The P300 speller is a brain-computer interface that enables people with neuromuscular disorders to communicate based on eliciting event-related potentials (ERP) in electroencephalo…
Conditional Central Limit Theorems for Gaussian Projections
Galen Reeves
This paper addresses the question of when projections of a high-dimensional random vector are approximately Gaussian. This problem has been studied previously in the context of hig…
The Replica-Symmetric Prediction for Compressed Sensing with Gaussian Matrices is Exact
Galen Reeves, Henry D. Pfister
This paper considers the fundamental limit of compressed sensing for i.i.d. signal distributions and i.i.d. Gaussian measurement matrices. Its main contribution is a rigorous chara…
Classification and Reconstruction of High-Dimensional Signals from Low-Dimensional Features in the Presence of Side Information
Francesco Renna, Liming Wang, Xin Yuan +5
This paper offers a characterization of fundamental limits on the classification and reconstruction of high-dimensional signals from low-dimensional features, in the presence of si…
On the Role of Diversity in Sparsity Estimation
Galen Reeves, Michael Gastpar
A major challenge in sparsity pattern estimation is that small modes are difficult to detect in the presence of noise. This problem is alleviated if one can observe samples from mu…
A Compressed Sensing Wire-Tap Channel
Galen Reeves, Naveen Goela, Nebojsa Milosavljevic +1
A multiplicative Gaussian wire-tap channel inspired by compressed sensing is studied. Lower and upper bounds on the secrecy capacity are derived, and shown to be relatively tight i…
The Sampling Rate-Distortion Tradeoff for Sparsity Pattern Recovery in Compressed Sensing
Galen Reeves, Michael Gastpar
Recovery of the sparsity pattern (or support) of an unknown sparse vector from a limited number of noisy linear measurements is an important problem in compressed sensing. In the h…
Approximate Sparsity Pattern Recovery: Information-Theoretic Lower Bounds
Galen Reeves, Michael Gastpar
Recovery of the sparsity pattern (or support) of an unknown sparse vector from a small number of noisy linear measurements is an important problem in compressed sensing. In this pa…
"Compressed" Compressed Sensing
Galen Reeves, Michael Gastpar
The field of compressed sensing has shown that a sparse but otherwise arbitrary vector can be recovered exactly from a small number of randomly constructed linear projections (or s…