Vector approximate message passing

S Rangan, P Schniter… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
The standard linear regression (SLR) problem is to recover a vector x 0 from noisy linear
observations y= Ax 0+ w. The approximate message passing (AMP) algorithm proposed by …

Plug-and-play methods for magnetic resonance imaging: Using denoisers for image recovery

R Ahmad, CA Bouman, GT Buzzard… - IEEE signal …, 2020 - ieeexplore.ieee.org
Magnetic resonance imaging (MRI) is a noninvasive diagnostic tool that provides excellent
soft-tissue contrast without the use of ionizing radiation. Compared to other clinical imaging …

Approximate message passing algorithms for rotationally invariant matrices

Z Fan - The Annals of Statistics, 2022 - projecteuclid.org
Approximate Message Passing algorithms for rotationally invariant matrices Page 1 The
Annals of Statistics 2022, Vol. 50, No. 1, 197–224 https://doi.org/10.1214/21-AOS2101 © …

Rigorous dynamics of expectation-propagation-based signal recovery from unitarily invariant measurements

K Takeuchi - IEEE Transactions on Information Theory, 2019 - ieeexplore.ieee.org
Signal recovery from unitarily invariant measurements is investigated in this paper. A
message-passing algorithm is formulated on the basis of expectation propagation (EP). A …

Vector approximate message passing for the generalized linear model

P Schniter, S Rangan… - 2016 50th Asilomar …, 2016 - ieeexplore.ieee.org
The generalized linear model (GLM), where a random vector x is observed through a noisy,
possibly nonlinear, function of a linear transform output z= Ax, arises in a range of …

Asymptotic errors for teacher-student convex generalized linear models (or: How to prove Kabashima's replica formula)

C Gerbelot, A Abbara, F Krzakala - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
There has been a recent surge of interest in the study of asymptotic reconstruction
performance in various cases of generalized linear estimation problems in the teacher …

Plug-in estimation in high-dimensional linear inverse problems: A rigorous analysis

AK Fletcher, P Pandit, S Rangan… - Advances in Neural …, 2018 - proceedings.neurips.cc
Estimating a vector $\mathbf {x} $ from noisy linear measurements $\mathbf {Ax+ w} $ often
requires use of prior knowledge or structural constraints on $\mathbf {x} $ for accurate …

A low-complexity massive MIMO detection based on approximate expectation propagation

X Tan, YL Ueng, Z Zhang, X You… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Among various massive multiple-input multiple-output (MIMO) signal detection schemes,
expectation propagation (EP) achieves superior performance in high-dimensional systems …

Generalization error of generalized linear models in high dimensions

M Emami, M Sahraee-Ardakan… - International …, 2020 - proceedings.mlr.press
At the heart of machine learning lies the question of generalizability of learned rules over
previously unseen data. While over-parameterized models based on neural networks are …

Inference with deep generative priors in high dimensions

P Pandit, M Sahraee-Ardakan… - IEEE Journal on …, 2020 - ieeexplore.ieee.org
Deep generative priors offer powerful models for complex-structured data, such as images,
audio, and text. Using these priors in inverse problems typically requires estimating the input …