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Paper Citation Record · LEDGER

Joint estimation of smooth graph signals from partial linear measurements

As of 17 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 1 inbound Pith citation observation for arXiv:2505.23240.

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

pith.paper-citation-record.v1
2505.23240 v2

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

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measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T20:49:03.810180Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

39 of 39 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 64cd4e23-eb73-43f7-8b09-1f01aa166de6 · outbound

This paper cites Dynamic ranking and translation synchronization.

Joint estimation of smooth graph signals from partial linear measurements Dynamic ranking and translation synchronization

Reference 1

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Observation 8855ac3c-9aa9-427a-b305-e36cae28a1d1 · outbound

This paper cites Belkin, I.

Joint estimation of smooth graph signals from partial linear measurements Belkin, I

Reference 2

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Observation 73606776-c516-4c20-97a5-d9e582064869 · outbound

This paper cites Concentration Inequalities: A Nonasymptotic Theory of Independence.

Joint estimation of smooth graph signals from partial linear measurements Concentration Inequalities: A Nonasymptotic Theory of Independence

Reference 3

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Observation 3bb4eb87-807c-46b0-a690-89ef877ac2a5 · outbound

This paper cites How fine-tuning allows for effective meta-learning.

Joint estimation of smooth graph signals from partial linear measurements How fine-tuning allows for effective meta-learning

Reference 4

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Observation 2c54140c-68a7-4733-854e-93dfefc41198 · outbound

This paper cites Cormen, Charles E.

Joint estimation of smooth graph signals from partial linear measurements Cormen, Charles E

Reference 5

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Source-reported events for the cited work

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Observation afbb7785-f38d-445c-a161-20c53b21c133 · outbound

This paper cites Dalalyan, Mohamed Hebiri, and Johannes Lederer.

Joint estimation of smooth graph signals from partial linear measurements Dalalyan, Mohamed Hebiri, and Johannes Lederer

Reference 6

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Observation fa67b608-0b31-433d-8d5b-b494625d45f1 · outbound

This paper cites On consistency of graph-based semi-supervised learning.

Joint estimation of smooth graph signals from partial linear measurements On consistency of graph-based semi-supervised learning

Reference 7

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Observation d3a89db8-ad93-4d7e-a8de-939ab5a06066 · outbound

This paper cites Few-Shot Learning via Learning the Representation, Provably.

Joint estimation of smooth graph signals from partial linear measurements Few-Shot Learning via Learning the Representation, Provably

Reference 8

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Source-reported events for the cited work

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Observation 54cc0f37-a1fe-474f-82c0-ed1557127123 · outbound

This paper cites Adaptive and robust multi-task learning.

Joint estimation of smooth graph signals from partial linear measurements Adaptive and robust multi-task learning

Reference 9

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Observation e75df56d-e1cf-42a5-b893-6b8d37154808 · outbound

This paper cites Minimax optimal regression over sobolev spaces via laplacian regularization on neighborhood graphs.

Joint estimation of smooth graph signals from partial linear measurements Minimax optimal regression over sobolev spaces via laplacian regularization on neighborhood graphs

Reference 10

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Observation f9107f11-4846-4073-8976-cdc6ad915aec · outbound

This paper cites van de Geer.

Joint estimation of smooth graph signals from partial linear measurements van de Geer

Reference 11

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Observation 62b92469-b5dd-4450-a66d-6a6fe3fa3bcb · outbound

This paper cites Horn and Charles R.

Joint estimation of smooth graph signals from partial linear measurements Horn and Charles R

Reference 12

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Observation 768f2055-5e6c-45d9-9c26-cbb33a0334bb · outbound

This paper cites A tail inequality for quadratic forms of subgaussian random vectors.

Joint estimation of smooth graph signals from partial linear measurements A tail inequality for quadratic forms of subgaussian random vectors

Reference 13

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Observation 7667f87e-d633-4a6f-9001-fa953ca7c1a2 · outbound

This paper cites Translation synchronization via truncated least squares.

Joint estimation of smooth graph signals from partial linear measurements Translation synchronization via truncated least squares

Reference 14

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This paper cites Optimal rates for total variation denoising.

Joint estimation of smooth graph signals from partial linear measurements Optimal rates for total variation denoising

Reference 15

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Source-reported events for the cited work

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Observation 7891785a-890f-4c4d-9923-a5ca480455e1 · outbound

This paper cites Kirichenko and H.

Joint estimation of smooth graph signals from partial linear measurements Kirichenko and H

Reference 16

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Source-reported events for the cited work

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Observation 3b231fde-918f-4d70-a98a-27f942e172b4 · outbound

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Joint estimation of smooth graph signals from partial linear measurements Kirichenko and H

Reference 17

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Observation 65061513-c369-4335-9bf3-02750ca25a00 · outbound

This paper cites Co-regularized multi-view spectral clustering.

Joint estimation of smooth graph signals from partial linear measurements Co-regularized multi-view spectral clustering

Reference 18

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Observation 2e59e502-f7ec-4e1c-aeb5-10dac1a066c4 · outbound

This paper cites Graph-based regularization for regression problems with alignment and highly correlated designs.

Joint estimation of smooth graph signals from partial linear measurements Graph-based regularization for regression problems with alignment and highly correlated designs

Reference 19

Resolution
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Source-reported events for the cited work

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Observation d2b2a51a-abb6-4933-b123-1cc3eaecb395 · outbound

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Joint estimation of smooth graph signals from partial linear measurements Locally adaptive regression splines

Reference 20

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Observation 17618de3-5870-4f4e-bb33-18e6cb9914f3 · outbound

This paper cites Probability and computing: Randomization and probabilistic techniques in algorithms and data analysis.

Joint estimation of smooth graph signals from partial linear measurements Probability and computing: Randomization and probabilistic techniques in algorithms and data analysis

Reference 21

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Joint estimation of smooth graph signals from partial linear measurements Semi-supervised learning with the graph laplacian: the limit of infinite unlabelled data

Reference 22

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Joint estimation of smooth graph signals from partial linear measurements Unresolved cited work

Reference 23

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Joint estimation of smooth graph signals from partial linear measurements Unresolved cited work

Reference 24

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Joint estimation of smooth graph signals from partial linear measurements Spectral and matrix factorization methods for consistent community detection in multi-layer networks

Reference 25

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Joint estimation of smooth graph signals from partial linear measurements Spectral and matrix factorization methods for consistent community detection in multi-layer networks

Reference 26

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Joint estimation of smooth graph signals from partial linear measurements Tibshirani

Reference 27

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Joint estimation of smooth graph signals from partial linear measurements Unresolved cited work

Reference 28

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Joint estimation of smooth graph signals from partial linear measurements Analysis of \ p\ -laplacian regularization in semisupervised learning

Reference 29

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Joint estimation of smooth graph signals from partial linear measurements Spectral and algebraic graph theory

Reference 30

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Joint estimation of smooth graph signals from partial linear measurements Learning from Similar Linear Representations: Adaptivity, Minimaxity, and Robustness

Reference 31

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Joint estimation of smooth graph signals from partial linear measurements The generalized elastic net for least squares regression with network-aligned signal and correlated design

Reference 32

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Joint estimation of smooth graph signals from partial linear measurements Provable meta-learning of linear representations

Reference 33

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Joint estimation of smooth graph signals from partial linear measurements Unresolved cited work

Reference 34

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Source-reported events for the cited work

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Observation cfa1bea6-a1b5-4308-8f90-ed63d7c24431 · outbound

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Joint estimation of smooth graph signals from partial linear measurements High-Dimensional Probability: An Introduction with Applications in Data Science

Reference 35

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Source-reported events for the cited work

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Observation 39d8d051-addd-4153-a14f-bd9713f7c169 · outbound

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Joint estimation of smooth graph signals from partial linear measurements Trend filtering on graphs

Reference 36

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Source-reported events for the cited work

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Observation a6e2519e-4b44-42f8-93a1-5663f078d6da · outbound

This paper cites Das asymptotische verteilungsgesetz der eigenwerte linearer partieller differentialgleichungen (mit einer anwendung auf die theorie der hohlraumstrahlung).

Joint estimation of smooth graph signals from partial linear measurements Das asymptotische verteilungsgesetz der eigenwerte linearer partieller differentialgleichungen (mit einer anwendung auf die theorie der hohlraumstrahlung)

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-07T13:02:19.619439Z

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source=arxiv_source observed=2026-08-07T13:02:19.619439Z digest=sha256:e31b51fa46faa45c68a3e7c8217bb16934e438493090727848159d339871d054

Observation 5e0694a9-7459-4ed5-9b0b-df11e1af1197 · outbound

This paper cites Learning from labeled and unlabeled data on a directed graph.

Joint estimation of smooth graph signals from partial linear measurements Learning from labeled and unlabeled data on a directed graph

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:02:20.632686Z

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source=arxiv_source observed=2026-08-07T13:02:19.705711Z digest=sha256:3e2cbe1006e88b7f03b835f3d6740407efd988af8b9ddec201c960bf66450634

Observation c68755a5-48c7-4d43-ab9f-dc113a0e6bf9 · outbound

This paper cites Semi-supervised learning using gaussian fields and harmonic functions.

Joint estimation of smooth graph signals from partial linear measurements Semi-supervised learning using gaussian fields and harmonic functions

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:02:20.368747Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T13:02:19.778036Z digest=sha256:2345b8b6c2eb38276489c2fcfb2a78372976ea49140de4e466878eaee5484b5a

Pith citing papers

Observation e64d9c8b-9d87-45ea-aeec-e62c7100f0ad · inbound

Joint learning of a network of linear dynamical systems via total variation penalization cites this paper.

Joint learning of a network of linear dynamical systems via total variation penalization Joint estimation of smooth graph signals from partial linear measurements

Reference 36

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no resolver link, observed 2026-08-03T20:49:03.810180Z

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