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08/28/2023
Distributed Dual Coordinate Ascent with Imbalanced Data on a General Tree Network
In this paper, we investigate the impact of imbalanced data on the conve...
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05/09/2023
To AI or not to AI, to Buy Local or not to Buy Local: A Mathematical Theory of Real Price
In the past several decades, the world's economy has become increasingly...
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11/03/2022
Optimal Compression for Minimizing Classification Error Probability: an Information-Theoretic Approach
We formulate the problem of performing optimal data compression under th...
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08/05/2020
Optimal Pooling Matrix Design for Group Testing with Dilution (Row Degree) Constraints
In this paper, we consider the problem of designing optimal pooling matr...
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07/29/2020
Error Correction Codes for COVID-19 Virus and Antibody Testing: Using Pooled Testing to Increase Test Reliability
We consider a novel method to increase the reliability of COVID-19 virus...
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07/28/2020
Derivation of Information-Theoretically Optimal Adversarial Attacks with Applications to Robust Machine Learning
We consider the theoretical problem of designing an optimal adversarial ...
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04/13/2020
Low-Cost and High-Throughput Testing of COVID-19 Viruses and Antibodies via Compressed Sensing: System Concepts and Computational Experiments
Coronavirus disease 2019 (COVID-19) is an ongoing pandemic infectious di...
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03/26/2020
Do Deep Minds Think Alike? Selective Adversarial Attacks for Fine-Grained Manipulation of Multiple Deep Neural Networks
Recent works have demonstrated the existence of adversarial examples ta...
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05/25/2019
Trust but Verify: An Information-Theoretic Explanation for the Adversarial Fragility of Machine Learning Systems, and a General Defense against Adversarial Attacks
Deep-learning based classification algorithms have been shown to be susc...
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03/10/2019
Fast Single Image Reflection Suppression via Convex Optimization
Removing undesired reflections from images taken through the glass is of...
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01/27/2019
An Information-Theoretic Explanation for the Adversarial Fragility of AI Classifiers
We present a simple hypothesis about a compression property of artificia...
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12/31/2018
How did Donald Trump Surprisingly Win the 2016 United States Presidential Election? an Information-Theoretic Perspective (Clean Sensing for Big Data Analytics:Optimal Strategie
Donald Trump was lagging behind in nearly all opinion polls leading up t...
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10/26/2018
Outlier Detection using Generative Models with Theoretical Performance Guarantees
This paper considers the problem of recovering signals from compressed m...
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04/09/2018
MSE-optimal 1-bit Precoding for Multiuser MIMO via Branch and Bound
In this paper, we solve the sum mean-squared error (MSE)-optimal 1-bit q...
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02/14/2018
Necessary and Sufficient Null Space Condition for Nuclear Norm Minimization in Low-Rank Matrix Recovery
Low-rank matrix recovery has found many applications in science and engi...
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11/30/2017
Symbol Error Rate Performance of Box-relaxation Decoders in Massive MIMO
The maximum-likelihood (ML) decoder for symbol detection in large multip...
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09/15/2015
Precise Phase Transition of Total Variation Minimization
Characterizing the phase transitions of convex optimizations in recoveri...
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03/10/2015
Robust recovery of complex exponential signals from random Gaussian projections via low rank Hankel matrix reconstruction
This paper explores robust recovery of a superposition of R distinct com...
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12/02/2013
Precise Semidefinite Programming Formulation of Atomic Norm Minimization for Recovering d-Dimensional (d≥ 2) Off-the-Grid Frequencies
Recent research in off-the-grid compressed sensing (CS) has demonstrated...
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07/17/2013
Universally Elevating the Phase Transition Performance of Compressed Sensing: Non-Isometric Matrices are Not Necessarily Bad Matrices
In compressed sensing problems, ℓ_1 minimization or Basis Pursuit was kn...
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06/11/2013
Precisely Verifying the Null Space Conditions in Compressed Sensing: A Sandwiching Algorithm
In this paper, we propose new efficient algorithms to verify the null sp...
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01/28/2013
Guarantees of Total Variation Minimization for Signal Recovery
In this paper, we consider using total variation minimization to recover...
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09/07/2008
