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08/15/2023
Max-affine regression via first-order methods
We consider regression of a max-affine model that produces a piecewise l...
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10/13/2022
Sketching low-rank matrices with a shared column space by convex programming
In many practical applications including remote sensing, multi-task lear...
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04/25/2022
Randomly Initialized Alternating Least Squares: Fast Convergence for Matrix Sensing
We consider the problem of reconstructing rank-one matrices from random ...
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10/28/2021
Approximately low-rank recovery from noisy and local measurements by convex program
Low-rank matrix models have been universally useful for numerous applica...
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03/12/2021
Max-Linear Regression by Scalable and Guaranteed Convex Programming
We consider the multivariate max-linear regression problem where the mod...
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04/06/2020
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...
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01/02/2020
Identifiability Conditions for Compressive Multichannel Blind Deconvolution
In applications such as multi-receiver radars and ultrasound array syste...
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10/25/2019
Phase Retrieval of Low-Rank Matrices by Anchored Regression
We study the low-rank phase retrieval problem, where we try to recover a...
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12/05/2017
Optimal Sample Complexity for Stable Matrix Recovery
Tremendous efforts have been made to study the theoretical and algorithm...
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11/30/2017
Blind Gain and Phase Calibration via Sparse Spectral Methods
Blind gain and phase calibration (BGPC) is a bilinear inverse problem in...
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06/28/2017
Generalized notions of sparsity and restricted isometry property. Part II: Applications
The restricted isometry property (RIP) is a universal tool for data reco...
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06/28/2017