Search Results for author:
Found 18 papers, 1 papers with code
Optimal Pooling Matrix Design for Group Testing with Dilution (Row Degree) Constraints
In this paper, we consider the problem of designing optimal pooling matrix for group testing (for example, for COVID-19 virus testing) with the constraint that no more than $r>0$ samples can be pooled together, which we call "dilution constraint".
Derivation of Information-Theoretically Optimal Adversarial Attacks with Applications to Robust Machine Learning
We consider the theoretical problem of designing an optimal adversarial attack on a decision system that maximally degrades the achievable performance of the system as measured by the mutual information between the degraded signal and the label of interest.
Do Deep Minds Think Alike? Selective Adversarial Attacks for Fine-Grained Manipulation of Multiple Deep Neural Networks
Recent works have demonstrated the existence of {\it adversarial examples} targeting a single machine learning system.
Trust but Verify: An Information-Theoretic Explanation for the Adversarial Fragility of Machine Learning Systems, and a General Defense against Adversarial Attacks
In this paper, we present a simple hypothesis about a feature compression property of artificial intelligence (AI) classifiers and present theoretical arguments to show that this hypothesis successfully accounts for the observed fragility of AI classifiers to small adversarial perturbations.
Fast Single Image Reflection Suppression via Convex Optimization
Removing undesired reflections from images taken through the glass is of great importance in computer vision.
An Information-Theoretic Explanation for the Adversarial Fragility of AI Classifiers
We present a simple hypothesis about a compression property of artificial intelligence (AI) classifiers and present theoretical arguments to show that this hypothesis successfully accounts for the observed fragility of AI classifiers to small adversarial perturbations.
How did Donald Trump Surprisingly Win the 2016 United States Presidential Election? an Information-Theoretic Perspective (Clean Sensing for Big Data Analytics:Optimal Strategies,Estimation Error Bounds Tighter than the Cramér-Rao Bound)
In this paper, we study the inaccuracies of opinion polls in the 2016 election through the lens of information theory.
Outlier Detection using Generative Models with Theoretical Performance Guarantees
In this paper, we propose a generative model neural network approach for reconstructing the ground truth signals under sparse outliers.
Necessary and Sufficient Null Space Condition for Nuclear Norm Minimization in Low-Rank Matrix Recovery
In [12, 14, 15], the authors established the necessary and sufficient null space conditions for nuclear norm minimization to recover every possible low-rank matrix with rank at most r (the strong null space condition).
Separation-Free Super-Resolution from Compressed Measurements is Possible: an Orthonormal Atomic Norm Minimization Approach
However, it is known that in order for TV minimization and atomic norm minimization to recover the missing data or the frequencies, the underlying $R$ frequencies are required to be well-separated, even when the measurements are noiseless.
Distributed Dual Coordinate Ascent in General Tree Networks and Communication Network Effect on Synchronous Machine Learning
Additionally, we show that adapting number of local and global iterations to network communication delays in the distributed dual coordinated ascent algorithm can improve its convergence speed.
Precise Phase Transition of Total Variation Minimization
Characterizing the phase transitions of convex optimizations in recovering structured signals or data is of central importance in compressed sensing, machine learning and statistics.
Projected Wirtinger Gradient Descent for Low-Rank Hankel Matrix Completion in Spectral Compressed Sensing
This paper considers reconstructing a spectrally sparse signal from a small number of randomly observed time-domain samples.
Robust recovery of complex exponential signals from random Gaussian projections via low rank Hankel matrix reconstruction
Our method can be applied to spectral compressed sensing where the signal of interest is a superposition of $R$ complex sinusoids.
Precise Semidefinite Programming Formulation of Atomic Norm Minimization for Recovering d-Dimensional ($d\geq 2$) Off-the-Grid Frequencies
Recent research in off-the-grid compressed sensing (CS) has demonstrated that, under certain conditions, one can successfully recover a spectrally sparse signal from a few time-domain samples even though the dictionary is continuous.
Universally Elevating the Phase Transition Performance of Compressed Sensing: Non-Isometric Matrices are Not Necessarily Bad Matrices
In this paper, we show that a polynomial-time algorithm can universally elevate the phase-transition performance of compressed sensing, compared with $\ell_1$ minimization, even for signals with constant-modulus nonzero elements.
Precisely Verifying the Null Space Conditions in Compressed Sensing: A Sandwiching Algorithm
In this paper, we first propose a series of new polynomial-time algorithms to compute upper bounds on $\alpha_k$.
Guarantees of Total Variation Minimization for Signal Recovery
In this paper, we consider using total variation minimization to recover signals whose gradients have a sparse support, from a small number of measurements.
