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As of 20 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:1908.04433.

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pith.paper-citation-record.v1
1908.04433 v2

Coverage vector

measured 36 of 36 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

36 of 36 outbound references displayed

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Outbound references

Observation eafa51eb-3c5b-46c7-80b0-4b8b13a330ca · outbound

This paper cites Monotonic central limit theorem for densities.

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Reference 1

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This paper cites The dynamics of message passing on dense graphs, with applications to compressed sensing.

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Reference 2

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This paper cites The lasso risk for gaussian matrices.

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Reference 3

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Reference 4

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This paper cites 1-bit compressive sensing.

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Reference 5

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This paper cites A generalized linear model with” gaussian” regressor variables.

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Reference 6

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This paper cites The phase transition for the existence of the maximum likelihood estimate in high-dimensional logistic regression.

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Reference 7

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This paper cites The convex geometry of linear inverse problems.Foundations of Computational Mathematics , 12(6):805–849, 2012.

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Reference 8

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This paper cites Phase Retrieval via Polytope Optimization: Geometry, Phase Transitions, and New Algorithms.

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Reference 9

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Observation 351c38b0-d17f-4a02-861b-c4f5e0e0c364 · outbound

This paper cites High dimensional robust m- estimation: Asymptotic variance via approximate message passing.

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Reference 10

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Observation 33b87a80-de9f-429b-8bad-e93d28645aad · outbound

This paper cites Message- passing algorithms for compressed sensing.

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Reference 11

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Observation eea96e55-7064-4c3d-8679-d0504f37aaa5 · outbound

This paper cites The noise- sensitivity phase transition in compressed sensing.

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Reference 12

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This paper cites On the impact of predictor geometry on the performance on high-dimensional ridge-regularized generalized robust regression estimators.

Sharp Guarantees for Solving Random Equations with One-Bit Information On the impact of predictor geometry on the performance on high-dimensional ridge-regularized generalized robust regression estimators

Reference 13

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This paper cites High-dimensional estimation of structured signals from non-linear observations with general convex loss functions.

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Reference 14

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Observation 14d63ed5-b4b9-46fb-9749-84a331abec4c · outbound

This paper cites Recovering Structured Data From Superimposed Non-Linear Measurements.

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Reference 15

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Reference 16

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Observation 0629d3e3-f30d-4f97-8487-25cbe72512c7 · outbound

This paper cites On Milman’s inequality and random subspaces which escape through a mesh in Rn.

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Reference 17

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This paper cites Robust 1-bit compressive sensing via binary stable embed- dings of sparse vectors.

Sharp Guarantees for Solving Random Equations with One-Bit Information Robust 1-bit compressive sensing via binary stable embed- dings of sparse vectors

Reference 18

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Sharp Guarantees for Solving Random Equations with One-Bit Information Asymptotic behavior of unregularized and ridge-regularized high-dimensional robust regression estimators : rigorous results

Reference 19

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This paper cites The Squared-Error of Generalized LASSO: A Precise Analysis.

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Reference 20

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Reference 21

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Reference 22

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Observation ada58688-0542-4ecb-a33d-f6199b34743e · outbound

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Sharp Guarantees for Solving Random Equations with One-Bit Information The generalized lasso with non-linear observations

Reference 23

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Reference 24

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Sharp Guarantees for Solving Random Equations with One-Bit Information The Impact of Regularization on High-dimensional Logistic Regression

Reference 25

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Sharp Guarantees for Solving Random Equations with One-Bit Information Various thresholds for $\ell_1$-optimization in compressed sensing

Reference 26

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Sharp Guarantees for Solving Random Equations with One-Bit Information A framework to characterize performance of LASSO algorithms

Reference 27

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Sharp Guarantees for Solving Random Equations with One-Bit Information A modern maximum-likelihood theory for high-dimensional logistic regression

Reference 28

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Sharp Guarantees for Solving Random Equations with One-Bit Information Lasso with non-linear measurements is equivalent to one with linear mea- surements

Reference 29

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Sharp Guarantees for Solving Random Equations with One-Bit Information Precise error analysis of regularized m-estimators in high dimensions

Reference 30

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Reference 31

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This paper cites The Generalized Lasso for Sub-gaussian Measurements with Dithered Quantization.

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Reference 32

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Unavailable: canonical work link unavailable.

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Reference 33

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Reference 34

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Reference 35

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-14T13:47:06.037496Z digest=sha256:ce1e4c701dee32f3d59e94a309570c8cecf7d9f118a42e7712128d6054df573d

Observation acbe86ec-cc01-444d-8a4e-09d83a892b0b · outbound

This paper cites an unresolved cited work.

Sharp Guarantees for Solving Random Equations with One-Bit Information Unresolved cited work

Reference 36

Resolution
unresolved
raw_fallback, observed 2026-08-14T13:47:06.213238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-14T13:47:06.041556Z digest=sha256:eed49bf7c0ecf35a8f3895c5ec2b189ab78b59f24406ec9cbec36ab880602d21

Pith citing papers

No inbound Pith citation observations are available.