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Exact and Stable Covariance Estimation From Quadratic Sampling via Convex Programming

https://doi.org/10.1109/tit.2015.2429594

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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A Simple Proof of the Restricted Isometry Property for Random Matrices

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Exact Matrix Completion via Convex Optimization

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Matrix perturbation theory

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The restricted isometry property and its implications for compressed sensing

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Introduction to the non-asymptotic analysis of random matrices

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Information Theory and Reliable Communication

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Recipes for Stable Linear Embeddings From Hilbert Spaces to $ {\mathbb {R}}^{m}$

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Preconditioned Data Sparsification for Big Data with Applications to PCA and K-means

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Convex and Nonconvex Formulations for Mixed Regression With Two Components: Minimax Optimal Rates

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Median-Truncated Nonconvex Approach for Phase Retrieval With Outliers

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Extreme Compressive Sampling for Covariance Estimation

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Sample-Efficient Algorithms for Recovering Structured Signals From Magnitude-Only Measurements

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Toward the Optimal Construction of a Loss Function Without Spurious Local Minima for Solving Quadratic Equations

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Sparse and Low-Rank Tensor Estimation via Cubic Sketchings

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Provable Low Rank Phase Retrieval

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Covariance Matrix Estimation With Non Uniform and Data Dependent Missing Observations

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Nonconvex Matrix Factorization From Rank-One Measurements

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Learning Mixtures of Low-Rank Models

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Sample-Measurement Tradeoff in Support Recovery Under a Subgaussian Prior

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Fast and Sample-Efficient Federated Low Rank Matrix Recovery From Column-Wise Linear and Quadratic Projections

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Sketching Sparse Low-Rank Matrices With Near-Optimal Sample- and Time-Complexity Using Message Passing

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Low-Rank Toeplitz Matrix Restoration: Descent Cone Analysis and Structured Random Matrix

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A Sample Efficient Alternating Minimization-Based Algorithm for Robust Phase Retrieval

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Near-Quadratic Convergence of the Gauss–Newton Method for Complex Phase Retrieval

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Effective Data-Aware Covariance Estimator From Compressed Data

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Compressive Phase Retrieval via Generalized Approximate Message Passing

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Generalized Coprime Sampling of Toeplitz Matrices for Spectrum Estimation

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Low-Rank Positive Semidefinite Matrix Recovery From Corrupted Rank-One Measurements

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Scalable Solvers of Random Quadratic Equations via Stochastic Truncated Amplitude Flow

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Gridless Line Spectrum Estimation and Low-Rank Toeplitz Matrix Compression Using Structured Samplers: A Regularization-Free Approach

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Subspace Learning From Bits

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Phase Retrieval via Reweighted Amplitude Flow

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Fast and Provable Algorithms for Learning Two-Layer Polynomial Neural Networks

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Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview

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Guaranteed Localization of More Sources Than Sensors With Finite Snapshots in Multiple Measurement Vector Models Using Difference Co-Arrays

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Deterministic Completion of Rectangular Matrices Using Asymmetric Ramanujan Graphs: Exact and Stable Recovery

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Solving Complex Quadratic Systems With Full-Rank Random Matrices

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Low-Rank Matrix Recovery With Scaled Subgradient Methods: Fast and Robust Convergence Without the Condition Number

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Sensor Network Localization via Riemannian Conjugate Gradient and Rank Reduction

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Intelligent Reflecting Surface-Assisted Adaptive Beamforming for Blind Interference Suppression

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Robust Low-Rank Tensor Train Recovery

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Phase Retrieval via Difference of Convex Programming

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Sample Efficient Toeplitz Covariance Estimation

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Stochastic Model-Based Minimization of Weakly Convex Functions

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Nonconvex Robust Low-Rank Matrix Recovery

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An Optimal-Storage Approach to Semidefinite Programming Using Approximate Complementarity

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ISLET: Fast and Optimal Low-Rank Tensor Regression via Importance Sketching

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Low-Rank Matrix Estimation from Rank-One Projections by Unlifted Convex Optimization

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Norm and Trace Estimation with Random Rank-one Vectors

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Communication-Efficient Distributed Eigenspace Estimation

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First-Order Methods for Nonsmooth Nonconvex Functional Constrained Optimization with or without Slater Points

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Sharpness and Conditioning of Nonsmooth Convex Formulations in Statistical Signal Recovery

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Almost Phase Retrieval with Few Intensity Measurements

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On the effect of model mismatch for sequential Info-Greedy Sensing

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ROP: Matrix recovery via rank-one projections

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Optimal rates of convergence for noisy sparse phase retrieval via thresholded Wirtinger flow

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A flexible convex relaxation for phase retrieval

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Nonconvex Factorization and Manifold Formulations Are Almost Equivalent in Low-Rank Matrix Optimization

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A Primal-Dual Smoothing Framework for Max-Structured Non-Convex Optimization

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Recursive Importance Sketching for Rank Constrained Least Squares: Algorithms and High-Order Convergence

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Memory and Computation Efficient PCA via Very Sparse Random Projections

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Randomized Algorithms for Large-Scale Data Analysis

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ROP inception: signal estimation with quadratic random sketching

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AltGDmin: Alternating GD and Minimization for Partly-decoupled (Federated) Optimization

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Fast Compressive Phase Retrieval under Bounded Noise

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Topics in learning sparse and low-rank models of non-negative data

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Fully Decentralized and Federated Low Rank Compressive Sensing

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Fast Quadratic Sensing Via Nonconvex Optimization

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Correlation matrix estimation of ordered data using sketches

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Robust Bilinear Form Identification: A Subgradient Method with Geometrically Decaying Stepsize in the Presence of Heavy-Tailed Noise

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Low rank phase retrieval

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Una Revisión de los Estimadores de Matrices de Bajo Rango y Matrices Dispersas

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Solving Systems of Quadratic Equations via Exponential-Type Gradient Descent Algorithm

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Proximal linearized iteratively reweighted least squares for a class of nonconvex and nonsmooth problems

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Regularization-free estimation in trace regression with symmetric positive semidefinite matrices

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The local convexity of solving systems of quadratic equations

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Recipes for stable linear embeddings from Hilbert spaces to R^m

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Channel Vector Subspace Estimation from Low-Dimensional Projections

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A Non-convex One-Pass Framework for Generalized Factorization Machine and Rank-One Matrix Sensing

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Improved Algorithms for Matrix Recovery from Rank-One Projections

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Solving Almost all Systems of Random Quadratic Equations

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Algorithmic Regularization in Over-parameterized Matrix Sensing and Neural Networks with Quadratic Activations

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Solving Systems of Quadratic Equations via Exponential-type Gradient Descent Algorithm

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Implicit regularization and solution uniqueness in over-parameterized matrix sensing

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Matrix Recovery from Rank-One Projection Measurements via Nonconvex Minimization

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Towards the optimal construction of a loss function without spurious local minima for solving quadratic equations

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Composite optimization for robust blind deconvolution

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Sparse Non-Negative Recovery from Biased Subgaussian Measurements using NNLS

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Provable Low Rank Phase Retrieval

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Universality in Learning from Linear Measurements

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Performance bound of the intensity-based model for noisy phase retrieval

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Spectral Frank-Wolfe Algorithm: Strict Complementarity and Linear Convergence

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Non-Convex Structured Phase Retrieval

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Estimation of Shortest Path Covariance Matrices

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Rank-One Measurements of Low-Rank PSD Matrices Have Small Feasible Sets

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Lecture notes on non-convex algorithms for low-rank matrix recovery

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Convex Latent Effect Logit Model via Sparse and Low-rank Decomposition

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