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09/14/2023
Uncertainty quantification for learned ISTA
Model-based deep learning solutions to inverse problems have attracted i...
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08/05/2023
Approximating Positive Homogeneous Functions with Scale Invariant Neural Networks
We investigate to what extent it is possible to solve linear inverse pro...
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06/27/2023
On the reconstruction of bandlimited signals from random samples quantized via noise-shaping
Noise-shaping quantization techniques are widely used for converting ban...
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03/17/2023
How robust is randomized blind deconvolution via nuclear norm minimization against adversarial noise?
In this paper, we study the problem of recovering two unknown signals fr...
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12/30/2022
Uncertainty quantification for sparse Fourier recovery
One of the most prominent methods for uncertainty quantification in high...
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12/30/2022
Non-intrusive surrogate modelling using sparse random features with applications in crashworthiness analysis
Efficient surrogate modelling is a key requirement for uncertainty quant...
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02/10/2022
Enhanced Digital Halftoning via Weighted Sigma-Delta Modulation
In this paper, we study error diffusion techniques for digital halftonin...
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11/24/2021
The Surprising Benefits of Hysteresis in Unlimited Sampling: Theory, Algorithms and Experiments
The Unlimited Sensing Framework (USF) was recently introduced to overcom...
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06/24/2021
Johnson-Lindenstrauss Embeddings with Kronecker Structure
We prove the Johnson-Lindenstrauss property for matrices Φ D_ξ where Φ h...
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06/08/2021
Proof methods for robust low-rank matrix recovery
Low-rank matrix recovery problems arise naturally as mathematical formul...
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05/12/2021
Sketching with Kerdock's crayons: Fast sparsifying transforms for arbitrary linear maps
Given an arbitrary matrix A∈ℝ^n× n, we consider the fundamental problem ...
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05/12/2021
Unlimited Sampling from Theory to Practice: Fourier-Prony Recovery and Prototype ADC
Following the Unlimited Sampling strategy to alleviate the omnipresent d...
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05/10/2021
The Modulo Radon Transform: Theory, Algorithms and Applications
Recently, experiments have been reported where researchers were able to ...
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03/03/2021
Riemannian thresholding methods for row-sparse and low-rank matrix recovery
In this paper, we present modifications of the iterative hard thresholdi...
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10/23/2020
On the robustness of noise-blind low-rank recovery from rank-one measurements
We prove new results about the robustness of well-known convex noise-bli...
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06/23/2020
A sample efficient sparse FFT for arbitrary frequency candidate sets in high dimensions
In this paper a sublinear time algorithm is presented for the reconstruc...
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06/17/2020
High order low-bit Sigma-Delta quantization for fusion frames
We construct high order low-bit Sigma-Delta (ΣΔ) quantizers for the vect...
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05/05/2020
On recovery guarantees for angular synchronization
The angular synchronization problem of estimating a set of unknown angle...
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04/09/2020
Well conditioned ptychograpic imaging via lost subspace completion
Ptychography, a special case of the phase retrieval problem, is a popula...
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11/14/2019
Predicting sparse circle maps from their dynamics
The problem of identifying a dynamical system from its dynamics is of gr...
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07/16/2019
On the geometry of polytopes generated by heavy-tailed random vectors
We study the geometry of centrally-symmetric random polytopes, generated...
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06/19/2019
Complex phase retrieval from subgaussian measurements
Phase retrieval refers to the problem of reconstructing an unknown vecto...
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05/10/2019
On Unlimited Sampling and Reconstruction
Shannon's sampling theorem is one of the cornerstone topics that is well...
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02/28/2019
On the convex geometry of blind deconvolution and matrix completion
Low-rank matrix recovery from structured measurements has been a topic o...
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07/17/2018
On Recovery Guarantees for One-Bit Compressed Sensing on Manifolds
This paper studies the problem of recovering a signal from one-bit compr...
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06/21/2018
Are good local minima wide in sparse recovery?
The idea of compressed sensing is to exploit representations in suitable...
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04/24/2018
Sparse Power Factorization: Balancing peakiness and sample complexity
In many applications, one is faced with an inverse problem, where the kn...
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12/05/2017
Optimal Fast Johnson-Lindenstrauss Embeddings for Large Data Sets
We introduce a new fast construction of a Johnson-Lindenstrauss matrix b...
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10/08/2012
