Paper Citation Record · LEDGER

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent

As of 11 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 2 inbound Pith citation observations for arXiv:2502.00463.

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2502.00463 v3

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Reference resolution

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Outbound references

Observation de4fe613-2ee9-4bd5-9bed-60c9e2379c1f · outbound

This paper cites Robust principal component analysis?.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Robust principal component analysis?

Reference 1

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This paper cites Cloud removal in remote sensing images using nonnegative matrix factorization and error correction,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Cloud removal in remote sensing images using nonnegative matrix factorization and error correction,

Reference 2

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This paper cites Low-rank phase retrieval,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Low-rank phase retrieval,

Reference 3

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This paper cites Sample-efficient low rank phase retrieval,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Sample-efficient low rank phase retrieval,

Reference 4

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This paper cites Robust and efficient high-dimensional quantum state tomography,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Robust and efficient high-dimensional quantum state tomography,

Reference 5

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This paper cites Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization,

Reference 6

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Observation f3fc64ff-398c-46b4-968b-cb6dae4449f9 · outbound

This paper cites Tight oracle inequalities for low-rank matrix recovery from a minimal number of noisy random measurements,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Tight oracle inequalities for low-rank matrix recovery from a minimal number of noisy random measurements,

Reference 7

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Observation 832b35d5-bcf3-46a5-bdd7-7318da968cf7 · outbound

This paper cites Exact matrix completion via convex opti- mization,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Exact matrix completion via convex opti- mization,

Reference 8

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This paper cites The power of convex relaxation: Near-optimal matrix completion,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent The power of convex relaxation: Near-optimal matrix completion,

Reference 9

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Observation 86d363ce-5cbd-4698-a743-3060db52b8af · outbound

This paper cites A nonlinear programming algorithm for solving semidefinite programs via low-rank factorization,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent A nonlinear programming algorithm for solving semidefinite programs via low-rank factorization,

Reference 10

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This paper cites Local minima and convergence in low-rank semidefinite pro- gramming,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Local minima and convergence in low-rank semidefinite pro- gramming,

Reference 11

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This paper cites Low-rank solutions of linear matrix equations via procrustes flow,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Low-rank solutions of linear matrix equations via procrustes flow,

Reference 12

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This paper cites On the computational and statistical complexity of over-parameterized matrix sensing,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent On the computational and statistical complexity of over-parameterized matrix sensing,

Reference 13

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Observation b0eac86f-b338-4754-a547-d9e886c46fa5 · outbound

This paper cites Global optimality of local search for low rank matrix recovery,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Global optimality of local search for low rank matrix recovery,

Reference 14

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Observation 16b6ebec-9709-4d91-bd8f-fcc2ec620c32 · outbound

This paper cites Understanding Incremental Learning of Gradient Descent: A Fine-grained Analysis of Matrix Sensing.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Understanding Incremental Learning of Gradient Descent: A Fine-grained Analysis of Matrix Sensing

Reference 15

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This paper cites Preconditioned gradient descent for over-parameterized nonconvex matrix factorization,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Preconditioned gradient descent for over-parameterized nonconvex matrix factorization,

Reference 16

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This paper cites Preconditioned gradient descent for overparameterized nonconvex burer–monteiro factorization with global optimality certification,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Preconditioned gradient descent for overparameterized nonconvex burer–monteiro factorization with global optimality certification,

Reference 17

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This paper cites A convergent gradient descent algorithm for rank minimization and semidefinite programming from random linear measurements,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent A convergent gradient descent algorithm for rank minimization and semidefinite programming from random linear measurements,

Reference 18

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This paper cites Solving 2d fredholm integral from incomplete measurements using compressive sensing,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Solving 2d fredholm integral from incomplete measurements using compressive sensing,

Reference 19

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This paper cites Accelerating ill-conditioned low-rank matrix estimation via scaled gradient descent,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Accelerating ill-conditioned low-rank matrix estimation via scaled gradient descent,

Reference 20

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Observation 8c8f81d2-fa8d-48b1-9c3c-1997a76c6367 · outbound

This paper cites The power of preconditioning in overparameterized low-rank matrix sensing,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent The power of preconditioning in overparameterized low-rank matrix sensing,

Reference 21

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This paper cites Accelerating gradient descent for over- parameterized asymmetric low-rank matrix sensing via preconditioning,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Accelerating gradient descent for over- parameterized asymmetric low-rank matrix sensing via preconditioning,

Reference 22

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This paper cites Fast and accurate estimation of low-rank matrices from noisy measurements via preconditioned non- convex gradient descent,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Fast and accurate estimation of low-rank matrices from noisy measurements via preconditioned non- convex gradient descent,

Reference 23

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This paper cites A Validation Approach to Over-parameterized Matrix and Image Recovery.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent A Validation Approach to Over-parameterized Matrix and Image Recovery

Reference 24

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Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Low-rank matrix completion using alternating minimization,

Reference 25

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Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Low rank matrix completion by alternating steep- est descent methods,

Reference 26

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Observation 135ca872-1aac-4360-a543-584cdfcbb155 · outbound

This paper cites Randomly initialized alternating least squares: Fast convergence for matrix sensing,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Randomly initialized alternating least squares: Fast convergence for matrix sensing,

Reference 27

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This paper cites Low rank matrix completion via robust alternating minimization in nearly linear time,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Low rank matrix completion via robust alternating minimization in nearly linear time,

Reference 28

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This paper cites Convergence of alternating gradient descent for matrix factorization,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Convergence of alternating gradient descent for matrix factorization,

Reference 29

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This paper cites Preconditioning matters: Fast global convergence of non-convex matrix factorization via scaled gradient descent,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Preconditioning matters: Fast global convergence of non-convex matrix factorization via scaled gradient descent,

Reference 30

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This paper cites Fast low-rank estimation by projected gradient descent: General statistical and algorithmic guarantees.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Fast low-rank estimation by projected gradient descent: General statistical and algorithmic guarantees

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Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Guaranteed matrix completion via non-convex factorization,

Reference 32

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Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent A Riemannian geometry for low-rank matrix completion

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Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Guarantees of riemannian optimization for low rank matrix recovery,

Reference 34

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Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Riemannian preconditioning,

Reference 35

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Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Accelerating sgd for highly ill- conditioned huge-scale online matrix completion,

Reference 36

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This paper cites A Preconditioned Riemannian Gradient Descent Algorithm for Low-Rank Matrix Recovery.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent A Preconditioned Riemannian Gradient Descent Algorithm for Low-Rank Matrix Recovery

Reference 37

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This paper cites Globally q-linear gauss- newton method for overparameterized non-convex matrix sensing,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Globally q-linear gauss- newton method for overparameterized non-convex matrix sensing,

Reference 38

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This paper cites Learned robust pca: A scalable deep unfolding approach for high-dimensional outlier detection,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Learned robust pca: A scalable deep unfolding approach for high-dimensional outlier detection,

Reference 39

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This paper cites Deeply Learned Robust Matrix Completion for Large-scale Low-rank Data Recovery.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Deeply Learned Robust Matrix Completion for Large-scale Low-rank Data Recovery

Reference 40

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This paper cites Low-rank matrix recovery with scaled subgradient methods: Fast and robust convergence without the condition number,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Low-rank matrix recovery with scaled subgradient methods: Fast and robust convergence without the condition number,

Reference 41

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Observation 97772a58-e7e0-417a-98e5-d8f9ab76fa20 · outbound

This paper cites Guarantees of a Preconditioned Subgradient Algorithm for Overparameterized Asymmetric Low-rank Matrix Recovery.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Guarantees of a Preconditioned Subgradient Algorithm for Overparameterized Asymmetric Low-rank Matrix Recovery

Reference 42

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This paper cites Algorithmic regularization in over- parameterized matrix sensing and neural networks with quadratic ac- tivations,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Algorithmic regularization in over- parameterized matrix sensing and neural networks with quadratic ac- tivations,

Reference 43

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Observation 6d9bd42c-679d-4aba-b4c5-e43148e4d363 · outbound

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

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Small random initialization is akin to spectral learning: Optimization and generalization guarantees for overparameterized low-rank matrix reconstruction,

Reference 44

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Observation eff7a8ff-e698-44f1-8cea-cb0d8917f0c2 · outbound

This paper cites Implicit balancing and regularization: Generalization and convergence guarantees for overpa- rameterized asymmetric matrix sensing,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Implicit balancing and regularization: Generalization and convergence guarantees for overpa- rameterized asymmetric matrix sensing,

Reference 45

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This paper cites Rank overspecified robust matrix recovery: Subgradient method and exact recovery,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Rank overspecified robust matrix recovery: Subgradient method and exact recovery,

Reference 46

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Observation ee6aecda-0aac-4194-b64b-cb5006724a81 · outbound

This paper cites How over-parameterization slows down gradient descent in matrix sensing: The curses of symmetry and initialization,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent How over-parameterization slows down gradient descent in matrix sensing: The curses of symmetry and initialization,

Reference 47

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Observation 5477690d-15ab-4508-be5e-f476ee8ac3ef · outbound

This paper cites Global Convergence of Sub-gradient Method for Robust Matrix Recovery: Small Initialization, Noisy Measurements, and Over-parameterization.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Global Convergence of Sub-gradient Method for Robust Matrix Recovery: Small Initialization, Noisy Measurements, and Over-parameterization

Reference 48

Resolution: verified exact; local_arxiv, observed 2026-08-09T19:04:34.093016Z

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Observation d5e61dc6-e813-4b77-8cd8-98b83b983731 · outbound

This paper cites Projected Gradient Descent Algorithm for Low-Rank Matrix Estimation.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Projected Gradient Descent Algorithm for Low-Rank Matrix Estimation

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-09T19:04:34.000402Z

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Observation 8a34eb27-c391-43e2-bf41-6e2f14f00545 · outbound

This paper cites Sharp restricted isometry property bounds for low-rank matrix recovery problems with corrupted measurements,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Sharp restricted isometry property bounds for low-rank matrix recovery problems with corrupted measurements,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:04:34.314784Z

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Observation b51d9cb8-b2bf-4107-9740-5e3f7667ab4a · outbound

This paper cites Noisy low-rank matrix optimization: Geometry of local minima and convergence rate,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Noisy low-rank matrix optimization: Geometry of local minima and convergence rate,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:04:34.305024Z

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Observation b6267daa-affe-4509-8b85-1188c384f56b · outbound

This paper cites A primal-dual analysis of global optimality in nonconvex low-rank matrix recovery,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent A primal-dual analysis of global optimality in nonconvex low-rank matrix recovery,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:04:34.294371Z

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Observation 815f46f7-d09d-407e-a55e-92010ef3e33f · outbound

This paper cites Geometric analysis of noisy low-rank matrix recovery in the exact parametrized and the overparametrized regimes,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Geometric analysis of noisy low-rank matrix recovery in the exact parametrized and the overparametrized regimes,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:04:34.283768Z

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Observation e7f4fe17-1773-4170-9757-4382936274b2 · outbound

This paper cites The global optimization geometry of low-rank matrix optimization,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent The global optimization geometry of low-rank matrix optimization,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:04:34.273714Z

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Observation fa85fa16-cfad-4d6c-821f-fe0e1dcd7d75 · outbound

This paper cites The non-convex geometry of low-rank matrix optimization,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent The non-convex geometry of low-rank matrix optimization,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:04:34.263291Z

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Observation 19575958-64e2-4545-a2cc-c1151fb0fd4f · outbound

This paper cites Global optimality in low-rank matrix optimization,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Global optimality in low-rank matrix optimization,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:04:34.252116Z

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Observation 09a99ef3-3d46-48b5-a24a-259c1b175c3d · outbound

This paper cites Guaranteed rank minimization via singular value projection,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Guaranteed rank minimization via singular value projection,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:04:34.240235Z

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Observation 34ba5c13-a369-491c-b4ce-a7f1b044a8fa · outbound

This paper cites Weighted low-rank approximations,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Weighted low-rank approximations,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:04:34.229362Z

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Observation ed22eb8a-8e6b-4a2e-85ad-04b939e6565e · outbound

This paper cites 1-bit matrix completion,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent 1-bit matrix completion,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:04:34.217631Z

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Observation 864f7283-079f-43a5-989b-5244080133ca · outbound

This paper cites Generalized Assorted Pixel Camera: Post-Capture Control of Resolution, Dynamic Range and Spectrum,.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Generalized Assorted Pixel Camera: Post-Capture Control of Resolution, Dynamic Range and Spectrum,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:04:34.205920Z

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Observation 47600fb0-8e90-4548-9f11-30335ebc8db0 · outbound

This paper cites an unresolved cited work.

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent Unresolved cited work

Reference 61

Resolution: unresolved; raw_fallback, observed 2026-08-09T19:04:34.194892Z

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Pith citing papers

Observation e8ac1fea-b0b2-4ec8-a957-ce53443b7a8a · inbound

Rethinking LoRA for Privacy-Preserving Federated Learning in Large Models cites this paper.

Rethinking LoRA for Privacy-Preserving Federated Learning in Large Models Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-15T20:10:18.230382Z

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Observation 904fdf8b-63f1-445e-bb68-54e2ebda7456 · inbound

Low-Rank Adaptation Redux for Large Models cites this paper.

Low-Rank Adaptation Redux for Large Models Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent

Reference 118

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:26:03.888560Z

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