A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing | Paper Citation Record

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This paper cites Bandeira and Ramon van Handel.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Bandeira and Ramon van Handel

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This paper cites The LASSO Risk for Gaussian Matrices.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing The LASSO Risk for Gaussian Matrices

Reference 2

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This paper cites Existence of solutions to the nonlinear equations characterizing the precise error of M-estimators.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Existence of solutions to the nonlinear equations characterizing the precise error of M-estimators

Reference 3

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This paper cites Bellec and Cun-Hui Zhang.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Bellec and Cun-Hui Zhang

Reference 4

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This paper cites The eigenvalues and eigenvectors of finite, low rank perturbations of large random matrices.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing The eigenvalues and eigenvectors of finite, low rank perturbations of large random matrices

Reference 5

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This paper cites State evolution for approximate message passing with non-separable functions.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing State evolution for approximate message passing with non-separable functions

Reference 6

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This paper cites Global Optimality of Local Search for Low Rank Matrix Recovery.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Global Optimality of Local Search for Low Rank Matrix Recovery

Reference 7

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This paper cites Monteiro.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Monteiro

Reference 8

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This paper cites Tony Cai and Anru Zhang.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Tony Cai and Anru Zhang

Reference 9

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This paper cites Tony Cai, Xiaodong Li, and Zongming Ma.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Tony Cai, Xiaodong Li, and Zongming Ma

Reference 10

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This paper cites Tony Cai, Tengyuan Liang, and Alexander Rakhlin.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Tony Cai, Tengyuan Liang, and Alexander Rakhlin

Reference 11

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Observation 95bb5d02-2fa6-421f-9092-e7ffc50a177f · outbound

This paper cites Candès and Yaniv Plan.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Candès and Yaniv Plan

Reference 12

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This paper cites Candès, Xiaodong Li, and Mahdi Soltanolkotabi.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Candès, Xiaodong Li, and Mahdi Soltanolkotabi

Reference 13

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This paper cites The Lasso with general Gaussian designs with applications to hypothesis testing.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing The Lasso with general Gaussian designs with applications to hypothesis testing

Reference 14

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This paper cites Sharp global convergence guarantees for iterative nonconvex optimization with random data.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Sharp global convergence guarantees for iterative nonconvex optimization with random data

Reference 15

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This paper cites Alternating minimization for generalized rank-1 matrix sensing: sharp predictions from a random initialization.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Alternating minimization for generalized rank-1 matrix sensing: sharp predictions from a random initialization

Reference 16

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This paper cites Low- Rank Matrix Recovery with Composite Optimization : Good Conditioning and Rapid Convergence.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Low- Rank Matrix Recovery with Composite Optimization : Good Conditioning and Rapid Convergence

Reference 17

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This paper cites Hanson– Wright inequality in Hilbert spaces with application to \ K \ -means clustering for non- Euclidean data.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Hanson– Wright inequality in Hilbert spaces with application to \ K \ -means clustering for non- Euclidean data

Reference 18

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Observation 8a7d3a6e-d68e-4cd4-a8e4-c07dcce92cfc · outbound

This paper cites Gradient Descent with Random Initialization : Fast Global Convergence for Nonconvex Phase Retrieval.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Gradient Descent with Random Initialization : Fast Global Convergence for Nonconvex Phase Retrieval

Reference 19

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This paper cites Inference and Uncertainty Quantification for Noisy Matrix Completion.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Inference and Uncertainty Quantification for Noisy Matrix Completion

Reference 20

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This paper cites Noisy Matrix Completion : Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Noisy Matrix Completion : Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization

Reference 21

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This paper cites Spectral Methods for Data Science : A Statistical Perspective.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Spectral Methods for Data Science : A Statistical Perspective

Reference 22

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This paper cites Bridging convex and nonconvex optimization in robust PCA : Noise , outliers and missing data.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Bridging convex and nonconvex optimization in robust PCA : Noise , outliers and missing data

Reference 23

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This paper cites Convex and Nonconvex Optimization Are Both Minimax - Optimal for Noisy Blind Deconvolution Under Random Designs.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Convex and Nonconvex Optimization Are Both Minimax - Optimal for Noisy Blind Deconvolution Under Random Designs

Reference 24

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This paper cites Lu, and Yuxin Chen.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Lu, and Yuxin Chen

Reference 25

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This paper cites Fast rates for noisy interpolation require rethinking the effect of inductive bias.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Fast rates for noisy interpolation require rethinking the effect of inductive bias

Reference 26

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This paper cites High dimensional robust M -estimation: asymptotic variance via approximate message passing.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing High dimensional robust M -estimation: asymptotic variance via approximate message passing

Reference 27

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This paper cites Lu, and Subhabrata Sen.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Lu, and Subhabrata Sen

Reference 28

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A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Unresolved cited work

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This paper cites Bickel, Chinghway Lim, and Bin Yu.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Bickel, Chinghway Lim, and Bin Yu

Reference 30

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A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Unresolved cited work

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A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing No Spurious Local Minima in Nonconvex Low Rank Problems : A Unified Geometric Analysis

Reference 32

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This paper cites Structured low-rank matrix factorization: Optimality, algorithm, and applications to image processing.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Structured low-rank matrix factorization: Optimality, algorithm, and applications to image processing

Reference 33

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This paper cites Noisy linear inverse problems under convex constraints: Exact risk asymptotics in high dimensions.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Noisy linear inverse problems under convex constraints: Exact risk asymptotics in high dimensions

Reference 34

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A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Entrywise dynamics and universality of general first order methods

Reference 35

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This paper cites Universality of regularized regression estimators in high dimensions.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Universality of regularized regression estimators in high dimensions

Reference 36

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This paper cites The distribution of Ridgeless least squares interpolators, July 2023.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing The distribution of Ridgeless least squares interpolators, July 2023

Reference 37

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This paper cites Confidence Intervals and Hypothesis Testing for High - Dimensional Regression.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Confidence Intervals and Hypothesis Testing for High - Dimensional Regression

Reference 38

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A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Hypothesis Testing in High - Dimensional Regression Under the Gaussian Random Design Model : Asymptotic Theory

Reference 39

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This paper cites Debiasing the lasso: Optimal sample size for Gaussian designs.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Debiasing the lasso: Optimal sample size for Gaussian designs

Reference 40

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This paper cites Precise statistical analysis of classification accuracies for adversarial training.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Precise statistical analysis of classification accuracies for adversarial training

Reference 41

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A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Precise Tradeoffs in Adversarial Training for Linear Regression

Reference 42

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This paper cites Phase transitions for the existence of unregularized M-estimators in single index models.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Phase transitions for the existence of unregularized M-estimators in single index models

Reference 43

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This paper cites Laurent and P.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Laurent and P

Reference 44

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This paper cites Symmetry, Saddle Points , and Global Optimization Landscape of Nonconvex Matrix Factorization.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Symmetry, Saddle Points , and Global Optimization Landscape of Nonconvex Matrix Factorization

Reference 45

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This paper cites A precise high-dimensional asymptotic theory for boosting and minimum-ell\_1-norm interpolated classifiers.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing A precise high-dimensional asymptotic theory for boosting and minimum-ell\_1-norm interpolated classifiers

Reference 46

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This paper cites Learning curves of generic features maps for realistic datasets with a teacher-student model.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Learning curves of generic features maps for realistic datasets with a teacher-student model

Reference 47

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This paper cites Nonconvex Matrix Factorization is Geodesically Convex: Global Landscape Analysis for Fixed-rank Matrix Optimization From a Riemannian Perspective.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Nonconvex Matrix Factorization is Geodesically Convex: Global Landscape Analysis for Fixed-rank Matrix Optimization From a Riemannian Perspective

Reference 48

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This paper cites Implicit Regularization in Nonconvex Statistical Estimation : Gradient Descent Converges Linearly for Phase Retrieval , Matrix Completion , and Blind Deconvolution.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Implicit Regularization in Nonconvex Statistical Estimation : Gradient Descent Converges Linearly for Phase Retrieval , Matrix Completion , and Blind Deconvolution

Reference 49

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This paper cites Beyond Procrustes : Balancing - Free Gradient Descent for Asymmetric Low - Rank Matrix Sensing.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Beyond Procrustes : Balancing - Free Gradient Descent for Asymmetric Low - Rank Matrix Sensing

Reference 50

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This paper cites Can Learning Be Explained By Local Optimality In Robust Low-rank Matrix Recovery?.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Can Learning Be Explained By Local Optimality In Robust Low-rank Matrix Recovery?

Reference 51

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This paper cites Geometric Analysis of Noisy Low - Rank Matrix Recovery in the Exact Parametrized and the Overparametrized Regimes.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Geometric Analysis of Noisy Low - Rank Matrix Recovery in the Exact Parametrized and the Overparametrized Regimes

Reference 52

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This paper cites The distribution of the Lasso : Uniform control over sparse balls and adaptive parameter tuning.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing The distribution of the Lasso : Uniform control over sparse balls and adaptive parameter tuning

Reference 53

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This paper cites Universality of max-margin classifiers.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Universality of max-margin classifiers

Reference 54

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Observation 5be1734b-6eba-4391-8352-e4a44a93c4a7 · outbound

This paper cites The generalization error of max-margin linear classifiers: Benign overfitting and high dimensional asymptotics in the overparametrized regime.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing The generalization error of max-margin linear classifiers: Benign overfitting and high dimensional asymptotics in the overparametrized regime

Reference 55

Resolution: verified exact; doi, observed 2026-08-06T22:55:24.097972Z

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Observation e5922092-5b44-40c2-aea7-ee1c121199e8 · outbound

This paper cites Wainwright.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Wainwright

Reference 56

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Observation 80314b2b-2fe9-468e-897e-26de053b28d4 · outbound

This paper cites The Impact of Regularization on High -dimensional Logistic Regression.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing The Impact of Regularization on High -dimensional Logistic Regression

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:26.204098Z

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Observation bda31723-273a-4e5f-8bf5-7e5a872e85d8 · outbound

This paper cites Implicit Balancing and Regularization : Generalization and Convergence Guarantees for Overparameterized Asymmetric Matrix Sensing.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Implicit Balancing and Regularization : Generalization and Convergence Guarantees for Overparameterized Asymmetric Matrix Sensing

Reference 58

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T22:55:24.953591Z

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Observation 9b4fba2e-a340-42bb-b368-8449a8cee819 · outbound

This paper cites Tight bounds for maximum \ ell\_1\ -margin classifiers.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Tight bounds for maximum \ ell\_1\ -margin classifiers

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:26.193155Z

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Observation e53aa29f-7a62-4fbb-a329-f3c86cc21faf · outbound

This paper cites Small random initialization is akin to spectral learning: Optimization and generalization guarantees for overparameterized low-rank matrix reconstruction.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Small random initialization is akin to spectral learning: Optimization and generalization guarantees for overparameterized low-rank matrix reconstruction

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:26.182331Z

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Observation 97222b9a-5f61-424b-805d-6477c03e39ec · outbound

This paper cites Sharp Asymptotics and Optimal Performance for Inference in Binary Models.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Sharp Asymptotics and Optimal Performance for Inference in Binary Models

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:26.171653Z

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Observation 77a04ba8-bdca-43ba-a756-c0af876fb35c · outbound

This paper cites Fundamental Limits of Ridge - Regularized Empirical Risk Minimization in High Dimensions.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Fundamental Limits of Ridge - Regularized Empirical Risk Minimization in High Dimensions

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:26.161172Z

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Observation d736a51a-9e50-42c7-85c3-05490af40660 · outbound

This paper cites The Gaussian min-max theorem in the Presence of Convexity.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing The Gaussian min-max theorem in the Presence of Convexity

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-06T22:55:23.994644Z

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Observation ad3af5d1-c18b-47d8-8259-56c7fa4b09d2 · outbound

This paper cites Regularized Linear Regression : A Precise Analysis of the Estimation Error.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Regularized Linear Regression : A Precise Analysis of the Estimation Error

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:26.148367Z

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Observation d29c4375-2857-49b2-9a50-b74c062c55ce · outbound

This paper cites Precise Error Analysis of Regularized M - Estimators in High Dimensions.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Precise Error Analysis of Regularized M - Estimators in High Dimensions

Reference 65

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T22:55:24.838249Z

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Observation 06e23ec8-5516-4031-9666-a50539e55dce · outbound

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

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Low- Rank Matrix Recovery With Scaled Subgradient Methods : Fast and Robust Convergence Without the Condition Number

Reference 66

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T22:55:24.742533Z

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Observation 4022de5e-2ece-4897-894a-72aa80174ce5 · outbound

This paper cites Low-rank Solutions of Linear Matrix Equations via Procrustes Flow.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Low-rank Solutions of Linear Matrix Equations via Procrustes Flow

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:26.137573Z

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Observation 5b3230df-1441-4662-9308-47d08431df42 · outbound

This paper cites Robust Matrix Completion with Heavy - Tailed Noise.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Robust Matrix Completion with Heavy - Tailed Noise

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-06T22:55:24.013731Z

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Observation f61aaacb-268e-40d8-9e04-4da1bb34e0fb · outbound

This paper cites Tight bounds for minimum \ ell\_1\ -norm interpolation of noisy data.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Tight bounds for minimum \ ell\_1\ -norm interpolation of noisy data

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:26.126149Z

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Observation bb9b9903-7934-40a2-bf99-9d5c3959784b · outbound

This paper cites Confidence Region of Singular Subspaces for Low - Rank Matrix Regression.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Confidence Region of Singular Subspaces for Low - Rank Matrix Regression

Reference 70

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T22:55:24.576263Z

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Observation 681853d7-a8a8-4f82-befb-2a53324faf36 · outbound

This paper cites The Power of Preconditioning in Overparameterized Low - Rank Matrix Sensing.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing The Power of Preconditioning in Overparameterized Low - Rank Matrix Sensing

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:26.115239Z

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Observation a3b29214-293a-44d0-9804-eb4677564edc · outbound

This paper cites Optimal Tuning - Free Convex Relaxation for Noisy Matrix Completion.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Optimal Tuning - Free Convex Relaxation for Noisy Matrix Completion

Reference 72

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T22:55:24.449300Z

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Observation 39df7551-7dbd-4995-8f5d-eeaf7a4eb136 · outbound

This paper cites an unresolved cited work.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Unresolved cited work

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-06T22:55:24.033049Z

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Observation c82708bc-4dfb-4b9d-8d66-33707f2aa1b5 · outbound

This paper cites Fast and Accurate Estimation of Low-Rank Matrices from Noisy Measurements via Preconditioned Non-Convex Gradient Descent.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Fast and Accurate Estimation of Low-Rank Matrices from Noisy Measurements via Preconditioned Non-Convex Gradient Descent

Reference 74

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:55:24.367234Z

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Observation f893e9da-4a6f-4fc2-94eb-29192966b9e6 · outbound

This paper cites Sharp Global Guarantees for Nonconvex Low-rank Recovery in the Noisy Overparameterized Regime.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Sharp Global Guarantees for Nonconvex Low-rank Recovery in the Noisy Overparameterized Regime

Reference 75

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:55:24.352906Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2752f660-78ed-4eaf-aa0c-6f004f8cef5e · outbound

This paper cites A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:26.104482Z

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Observation b077df3a-bd28-4981-88d0-59616545f026 · outbound

This paper cites write newline.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing write newline

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-06T22:55:24.047572Z

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