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Found 11 papers, 1 papers with code
Small-Noise Sensitivity Analysis of Locating Pulses in the Presence of Adversarial Perturbation
In the small noise regime, the local Lipschitz constant converges to the spectral norm of the noiseless Jacobian of the inverse map.
Stable estimation of pulses of unknown shape from multiple snapshots via ESPRIT
ESPRIT algorithm has been used to estimate the pulse locations.
Max-affine regression via first-order methods
We consider regression of a max-affine model that produces a piecewise linear model by combining affine models via the max function.
Randomly Initialized Alternating Least Squares: Fast Convergence for Matrix Sensing
In this paper, we show that ALS with random initialization converges to the true solution with $\varepsilon$-accuracy in $O(\log n + \log (1/\varepsilon)) $ iterations using only a near-optimal amount of samples, where we assume the measurement matrices to be i. i. d.
Max-Linear Regression by Convex Programming
When the $k$ linear components are equally likely to achieve the maximum, our result shows a sufficient number of noise-free observations for exact recovery scales as {$k^{4}p$} up to a logarithmic factor.
Low-Rank Matrix Estimation From Rank-One Projections by Unlifted Convex Optimization
We study an estimator with a convex formulation for recovery of low-rank matrices from rank-one projections.
Convolutional Framework for Accelerated Magnetic Resonance Imaging
Magnetic Resonance Imaging (MRI) is a noninvasive imaging technique that provides exquisite soft-tissue contrast without using ionizing radiation.
Decentralized sketching of low rank matrices
We address a low-rank matrix recovery problem where each column of a rank-r matrix X of size (d1, d2) is compressed beyond the point of recovery to size L with L << d1.
Blind Gain and Phase Calibration via Sparse Spectral Methods
We also show that our power iteration algorithms for BGPC compare favorably with competing algorithms in adversarial conditions, e. g., with noisy measurement or with a bad initial estimate.
Generalized notions of sparsity and restricted isometry property. Part II: Applications
The restricted isometry property (RIP) is a universal tool for data recovery.
Generalized notions of sparsity and restricted isometry property. Part I: A unified framework
The restricted isometry property (RIP) is an integral tool in the analysis of various inverse problems with sparsity models.
