Noisy pooled PCR for virus testing

J Zhu, K Rivera, D Baron - arxiv preprint arxiv:2004.02689, 2020‏ - arxiv.org
Fast testing can help mitigate the coronavirus disease 2019 (COVID-19) pandemic. Despite
their accuracy for single sample analysis, infectious diseases diagnostic tools, like RT-PCR …

A robust parallel algorithm for combinatorial compressed sensing

R Mendoza-Smith, JW Tanner… - IEEE Transactions on …, 2018‏ - ieeexplore.ieee.org
It was shown in previous work that a vector x ER n with at most k<; n nonzeros can be
recovered from an expander sketch Ax in O (nnz (A) log k) operations via the parallel-I 0 …

Performance trade-offs in multi-processor approximate message passing

J Zhu, A Beirami, D Baron - 2016 IEEE International …, 2016‏ - ieeexplore.ieee.org
We consider large-scale linear inverse problems in Bayesian settings. Our general
approach follows a recent line of work that applies the approximate message passing (AMP) …

History: An efficient and robust algorithm for noisy 1-bit compressed sensing

B Sun, H Feng, X Xu - IEICE TRANSACTIONS on Information and …, 2016‏ - search.ieice.org
We consider the problem of sparse signal recovery from 1-bit measurements. Due to the
noise present in the acquisition and transmission process, some quantized bits may be …

Optimal trade-offs in multi-processor approximate message passing

J Zhu, D Baron, A Beirami - arxiv preprint arxiv:1601.03790, 2016‏ - arxiv.org
We consider large-scale linear inverse problems in Bayesian settings. We follow a recent
line of work that applies the approximate message passing (AMP) framework to multi …

Statistical physics and information theory perspectives on linear inverse problems

J Zhu - arxiv preprint arxiv:1705.05070, 2017‏ - arxiv.org
Many real-world problems in machine learning, signal processing, and communications
assume that an unknown vector $ x $ is measured by a matrix A, resulting in a vector $ y …

Compressed sensing for graph signals

A Tawfik - 2018‏ - repositum.tuwien.at
In this thesis we are going to tackle the problem of estimating a sparse graph signal with an
unknown frequency support set of known size K from a sampled noisy version. Not knowing …

[ספר][B] Solving Large-Scale Inverse Problems via Approximate Message Passing and Optimization

Y Ma - 2017‏ - search.proquest.com
Page 1 ABSTRACT MA, YANTING. Solving Large-Scale Inverse Problems via Approximate
Message Passing and Optimization. (Under the direction of Dror Baron.) This work studies the …

Compressed sensing recovery with Bayesian approximate message passing using empirical least squares estimation without an explicit prior

A Tajjar - 2017‏ - repositum.tuwien.at
Compressed Sensing (CS) is a signal processing technique that allows for high-quality
reconstruction of a source signal vector of dimension N from a number MN of linear …

Minimax Compressed Sensing Reconstruction

L Dai, D Baron, H Krim - IEEE Transactions on Information Theory, 2014‏ - apps.dtic.mil
Final Report: Minimax Compressed Sensing Reconstruction Page 1 Standard Form 298 (Rev
8/98) Prescribed by ANSI Std. Z39.18 Final Report 62483-CS-SR.25 919-549-4350 …