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Paper Citation Record · LEDGER

Statistical Inference for Low-Rank Tensor Models

As of 18 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 3 inbound Pith citation observations for arXiv:2501.16223.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2501.16223 v1

Coverage vector

measured 58 of 58 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T13:43:07.846233Z

measured 61 of 61 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T04:53:49.182442Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-20T07:23:06.988874Z

Reference resolution

58 of 58 outbound references displayed

  • verified exact2
  • verified fuzzy16
  • unresolved40
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c551f5e7-52d9-4baf-b4c0-3dc588948500 · outbound

This paper cites Statistical Inference for Low-Rank Tensors: Heteroskedasticity, Subgaussianity, and Applications.

Statistical Inference for Low-Rank Tensor Models Statistical Inference for Low-Rank Tensors: Heteroskedasticity, Subgaussianity, and Applications

Reference 1

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Observation 3a2a7e00-0a26-4430-906f-d41db524e0f3 · outbound

This paper cites an unresolved cited work.

Statistical Inference for Low-Rank Tensor Models Unresolved cited work

Reference 2

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This paper cites an unresolved cited work.

Statistical Inference for Low-Rank Tensor Models Unresolved cited work

Reference 3

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Statistical Inference for Low-Rank Tensor Models Unresolved cited work

Reference 4

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Observation ae8c3921-bc50-4675-8b82-62bb34d5703e · outbound

This paper cites T., Liang, T., and Rakhlin, A.

Statistical Inference for Low-Rank Tensor Models T., Liang, T., and Rakhlin, A

Reference 5

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Statistical Inference for Low-Rank Tensor Models Unresolved cited work

Reference 6

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Statistical Inference for Low-Rank Tensor Models Unresolved cited work

Reference 7

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Statistical Inference for Low-Rank Tensor Models Unresolved cited work

Reference 8

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Statistical Inference for Low-Rank Tensor Models Unresolved cited work

Reference 9

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This paper cites H., Goldstein, L., and Shao, Q.-M.

Statistical Inference for Low-Rank Tensor Models H., Goldstein, L., and Shao, Q.-M

Reference 10

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Statistical Inference for Low-Rank Tensor Models Unresolved cited work

Reference 11

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Reference 12

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Statistical Inference for Low-Rank Tensor Models Unresolved cited work

Reference 13

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Statistical Inference for Low-Rank Tensor Models The total variation distance between high-dimensional Gaussians with the same mean

Reference 14

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Statistical Inference for Low-Rank Tensor Models Unresolved cited work

Reference 15

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Observation 0c24be72-e57c-4bcf-93cf-eb281aa208d4 · outbound

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Statistical Inference for Low-Rank Tensor Models Unresolved cited work

Reference 16

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Statistical Inference for Low-Rank Tensor Models A., and Peng, T

Reference 17

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Reference 18

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Observation 581b6613-8a92-4b39-b468-4993a3ed31ef · outbound

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Statistical Inference for Low-Rank Tensor Models and Oseledets, I

Reference 19

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Reference 21

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Statistical Inference for Low-Rank Tensor Models Unresolved cited work

Reference 24

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Statistical Inference for Low-Rank Tensor Models Unresolved cited work

Reference 25

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Reference 29

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Statistical Inference for Low-Rank Tensor Models and Zhang, A

Reference 31

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Statistical Inference for Low-Rank Tensor Models Statistical Inference in Tensor Completion: Optimal Uncertainty Quantification and Statistical-to-Computational Gaps

Reference 32

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Reference 33

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Statistical Inference for Low-Rank Tensor Models Tensor-based Collaborative Filtering With Smooth Ratings Scale

Reference 34

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Statistical Inference for Low-Rank Tensor Models Unresolved cited work

Reference 35

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Statistical Inference for Low-Rank Tensor Models Quantile and pseudo-Huber Tensor Decomposition

Reference 36

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Reference 37

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Observation c50d67f4-5db1-4495-a21f-c9ab31a4202b · outbound

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Statistical Inference for Low-Rank Tensor Models Bayesian tensor regression using the Tucker decomposition for sparse spatial modeling

Reference 38

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This paper cites and Suzuki, T.

Statistical Inference for Low-Rank Tensor Models and Suzuki, T

Reference 39

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Statistical Inference for Low-Rank Tensor Models and Townsend, A

Reference 40

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This paper cites Introduction to the non-asymptotic analysis of random matrices.

Statistical Inference for Low-Rank Tensor Models Introduction to the non-asymptotic analysis of random matrices

Reference 41

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Statistical Inference for Low-Rank Tensor Models Unresolved cited work

Reference 42

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Statistical Inference for Low-Rank Tensor Models and Fan, J

Reference 43

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Statistical Inference for Low-Rank Tensor Models Unresolved cited work

Reference 44

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Statistical Inference for Low-Rank Tensor Models Unresolved cited work

Reference 45

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Statistical Inference for Low-Rank Tensor Models and Yuan, M

Reference 46

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Statistical Inference for Low-Rank Tensor Models and Yuan, M

Reference 47

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Reference 48

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Statistical Inference for Low-Rank Tensor Models R., and Zhou, Y

Reference 49

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Reference 50

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Statistical Inference for Low-Rank Tensor Models Unresolved cited work

Reference 51

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Statistical Inference for Low-Rank Tensor Models and Han, R

Reference 52

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Statistical Inference for Low-Rank Tensor Models and Xia, D

Reference 53

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Statistical Inference for Low-Rank Tensor Models R., Luo, Y., Raskutti, G., and Yuan, M

Reference 54

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Reference 55

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Reference 56

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Statistical Inference for Low-Rank Tensor Models Statistical Inference for Low-Rank Tensor Models

Reference 57

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Statistical Inference for Low-Rank Tensor Models +∞X k3=2 SG3,k3 E(0) 3 U3 ⊗ U2 # G⊤ 1 G1G⊤ 1 − 1 2 U ⊤ 1 | {z } III.III . Here, first, similar to III .I in the proof of (184), we have III.I ≤

Reference 58

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Pith citing papers

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Bridging Domain Adaptation and Graph Neural Networks: A Tensor-Based Framework for Effective Label Propagation cites this paper.

Bridging Domain Adaptation and Graph Neural Networks: A Tensor-Based Framework for Effective Label Propagation Statistical Inference for Low-Rank Tensor Models

Reference 87

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Generalized Tensor Completion with Non-Random Missingness cites this paper.

Generalized Tensor Completion with Non-Random Missingness Statistical Inference for Low-Rank Tensor Models

Reference 40

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Dual-Channel Tensor Neural Networks: Finite-Sample Theory and Conformal Structure Selection cites this paper.

Dual-Channel Tensor Neural Networks: Finite-Sample Theory and Conformal Structure Selection Statistical Inference for Low-Rank Tensor Models

Reference 24

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