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06/30/2023
A Simple Proof for Efficient Federated Low Rank Matrix Recovery from column-wise Linear Projections
This note provides a significantly simpler and shorter proof of our samp...
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04/18/2022
Fully-Decentralized Alternating Projected Gradient Descent for Low Rank column-wise Compressive Sensing
This work develops a provably accurate fully-decentralized fast and comm...
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12/26/2021
Fully Decentralized and Federated Low Rank Compressive Sensing
In this work we develop a fully decentralized, federated, and fast solut...
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02/20/2021
Fast and Sample-Efficient Federated Low Rank Matrix Recovery from Column-wise Linear and Quadratic Projections
This work studies the following problem and its magnitude-only extension...
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06/23/2020
Non-Convex Structured Phase Retrieval
Phase retrieval (PR), also sometimes referred to as quadratic sensing, i...
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06/14/2020
Fast Robust Subspace Tracking via PCA in Sparse Data-Dependent Noise
This work studies the robust subspace tracking (ST) problem. Robust ST c...
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06/11/2020
Sample-Efficient Low Rank Phase Retrieval
In this paper we obtain an improved guarantee for the Low Rank Phase Ret...
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02/28/2020
Federated Over-the-Air Subspace Learning from Incomplete Data
Federated learning refers to a distributed learning scenario in which us...
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07/18/2019
Random Convolutional Coding for Robust and Straggler Resilient Distributed Matrix Computation
Distributed matrix computations (matrix-vector and matrix-matrix multipl...
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02/13/2019
Phaseless Low Rank Matrix Recovery and Subspace Tracking
This work introduces the first simple and provably correct solution for ...
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10/06/2018
Subspace Tracking from Missing and Outlier Corrupted Data
We study the related problems of subspace tracking in the presence of mi...
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09/11/2018
Phaseless Subspace Tracking
This work takes the first steps towards solving the "phaseless subspace ...
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03/01/2018
Static and Dynamic Robust PCA via Low-Rank + Sparse Matrix Decomposition: A Review
Principal Components Analysis (PCA) is one of the most widely used dimen...
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12/17/2017
MEDRoP: Memory-Efficient Dynamic Robust PCA
Robust PCA (RPCA) is the problem of separating a given data matrix into ...
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11/26/2017
Robust PCA and Robust Subspace Tracking
Principal Components Analysis (PCA) is one of the most widely used dimen...
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10/05/2017
Video Denoising and Enhancement via Dynamic Video Layering
Video denoising refers to the problem of removing "noise" from a video s...
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09/19/2017
Finite Sample Guarantees for PCA in Non-Isotropic and Data-Dependent Noise
This work obtains novel finite sample guarantees for Principal Component...
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05/24/2017
Provable Dynamic Robust PCA or Robust Subspace Tracking
Dynamic robust PCA refers to the dynamic (time-varying) extension of the...
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02/10/2017
PCA in Data-Dependent Noise (Correlated-PCA): Nearly Optimal Finite Sample Guarantees
We study Principal Component Analysis (PCA) in a setting where a part of...
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10/28/2016
Correlated-PCA: Principal Components' Analysis when Data and Noise are Correlated
Given a matrix of observed data, Principal Components Analysis (PCA) com...
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03/11/2015
Online Matrix Completion and Online Robust PCA
This work studies two interrelated problems - online robust PCA (RPCA) a...
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08/16/2011
Exact Reconstruction Conditions for Regularized Modified Basis Pursuit
In this correspondence, we obtain exact recovery conditions for regulari...
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10/04/2010
