Paper Citation Record · LEDGER

Nash: Neural Adaptive Shrinkage for Structured High-Dimensional Regression

As of 7 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2505.11143.

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

pith.paper-citation-record.v1
2505.11143 v2

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-22T14:56:26.085871Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

22 of 22 outbound references displayed

verified exact2
verified fuzzy12
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 58177687-e28a-463c-ac36-5385463c18ba · outbound

This paper cites URL https://www.jstor.org/stable/2984875.

Nash: Neural Adaptive Shrinkage for Structured High-Dimensional Regression URL https://www.jstor.org/stable/2984875

Reference 1

Resolution: verified exact; doi, observed 2026-05-22T14:56:42.038730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ed6a66e3-582a-4983-915d-88651cce4ffc · outbound

This paper cites URL https://ieeexplore.ieee.org/document/ 1163188.

Nash: Neural Adaptive Shrinkage for Structured High-Dimensional Regression URL https://ieeexplore.ieee.org/document/ 1163188

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-22T14:56:42.034425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 4e0bb74c-c350-4f80-a813-978b8f317730 · outbound

This paper cites A.1 Update for qβj Note that given that when qb, g(.; ., θ) are fixed then the objective for qβj j = 1.

Nash: Neural Adaptive Shrinkage for Structured High-Dimensional Regression A.1 Update for qβj Note that given that when qb, g(.; ., θ) are fixed then the objective for qβj j = 1

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T14:56:42.524538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c7be6efc-b317-463f-b9bc-33237eec4ea4 · outbound

This paper cites an unresolved cited work.

Nash: Neural Adaptive Shrinkage for Structured High-Dimensional Regression Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-05-22T14:56:42.528216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3625a45e-be46-4b59-ae32-3b30a42526e1 · outbound

This paper cites [2024]), so the and so q∗ βj = maxF (qbj) is given by computing the posterior of the following simple model ¯rj =xjβj + ε (33) βj ∼N (¯bj, σ2.

Nash: Neural Adaptive Shrinkage for Structured High-Dimensional Regression [2024]), so the and so q∗ βj = maxF (qbj) is given by computing the posterior of the following simple model ¯rj =xjβj + ε (33) βj ∼N (¯bj, σ2

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T14:56:42.520168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 808be939-c419-4e03-9459-2a151d16d6a3 · outbound

This paper cites In practice given that the column of X are centered xt jxt j = n − 1 for al j = 1.

Nash: Neural Adaptive Shrinkage for Structured High-Dimensional Regression In practice given that the column of X are centered xt jxt j = n − 1 for al j = 1

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T14:56:42.450043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation fb969dd2-c1c0-49fc-b12d-474fd2c55ae8 · outbound

This paper cites borrow information.

Nash: Neural Adaptive Shrinkage for Structured High-Dimensional Regression borrow information

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T14:56:42.516239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 65401300-dc4b-4c20-b303-9b59957811e0 · outbound

This paper cites Compute ˆθ := argmax θ ∈ Rm L(θ), (40) where L(θ) denotes the marginal likelihood, L(θ) := p( ¯β | s, θ, D) = pY j=1 R N ( ¯βj; bj, σ2.

Nash: Neural Adaptive Shrinkage for Structured High-Dimensional Regression Compute ˆθ := argmax θ ∈ Rm L(θ), (40) where L(θ) denotes the marginal likelihood, L(θ) := p( ¯β | s, θ, D) = pY j=1 R N ( ¯βj; bj, σ2

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T14:56:42.483327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 60af0558-2bbe-4734-8663-6097d0af32a7 · outbound

This paper cites ¯βp), s = (s1.

Nash: Neural Adaptive Shrinkage for Structured High-Dimensional Regression ¯βp), s = (s1

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T14:56:42.474348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation db847f79-c8fb-4986-a324-398ac5eab02c · outbound

This paper cites an unresolved cited work.

Nash: Neural Adaptive Shrinkage for Structured High-Dimensional Regression Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-05-22T14:56:42.459581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0eb8f026-3412-4b18-a096-1e091efa6549 · outbound

This paper cites Compute summaries from the posterior distributions, such as the posterior means ¯bj := E[bj | ¯βj, sj, ˆθ, D], using the estimated prior, p(bj | ¯βj, sj, ¯θ, D) ∝ N ( ˆβj; bj, σ2.

Nash: Neural Adaptive Shrinkage for Structured High-Dimensional Regression Compute summaries from the posterior distributions, such as the posterior means ¯bj := E[bj | ¯βj, sj, ˆθ, D], using the estimated prior, p(bj | ¯βj, sj, ¯θ, D) ∝ N ( ˆβj; bj, σ2

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T14:56:42.512474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b89fd1d0-12b9-44c6-a68e-cfcc1853b57d · outbound

This paper cites an unresolved cited work.

Nash: Neural Adaptive Shrinkage for Structured High-Dimensional Regression Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-05-22T14:56:42.501634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation eeb8ecb0-ee5d-472b-b134-10538fd07fe2 · outbound

This paper cites While Xie [2023] studies a different problem (smoothing Poisson counts), we draw inspiration from this work and adapt it to the high-dimensional Gaussian setting.

Nash: Neural Adaptive Shrinkage for Structured High-Dimensional Regression While Xie [2023] studies a different problem (smoothing Poisson counts), we draw inspiration from this work and adapt it to the high-dimensional Gaussian setting

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T14:56:42.487227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f0181940-4fa1-4a38-8da5-ba1b50d4b04a · outbound

This paper cites an unresolved cited work.

Nash: Neural Adaptive Shrinkage for Structured High-Dimensional Regression Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-05-22T14:56:42.470522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a657bd7b-2667-413b-9572-20d59c477219 · outbound

This paper cites B.1 Objective for bj in the Nash model The introduction of latent variable βj induces a marginal density of bj as log p(y|x, bj, σ2, σ2.

Nash: Neural Adaptive Shrinkage for Structured High-Dimensional Regression B.1 Objective for bj in the Nash model The introduction of latent variable βj induces a marginal density of bj as log p(y|x, bj, σ2, σ2

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T14:56:42.440994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0e4503e9-19cb-43eb-9c71-9d51107e63be · outbound

This paper cites an unresolved cited work.

Nash: Neural Adaptive Shrinkage for Structured High-Dimensional Regression Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-05-22T14:56:42.478805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c9643bff-8bf2-4cbf-915b-238450caf361 · outbound

This paper cites Before demonstrating that Nash optimizes a lower bound for the mr.ash approximation, we first introduce a lemma.

Nash: Neural Adaptive Shrinkage for Structured High-Dimensional Regression Before demonstrating that Nash optimizes a lower bound for the mr.ash approximation, we first introduce a lemma

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T14:56:42.494334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6a7b01a8-d005-428a-9fa3-f1424429addc · outbound

This paper cites 17 Proof.

Nash: Neural Adaptive Shrinkage for Structured High-Dimensional Regression 17 Proof

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T14:56:42.445827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation aca319ae-e3db-4bb8-b439-6f98c654a694 · outbound

This paper cites an unresolved cited work.

Nash: Neural Adaptive Shrinkage for Structured High-Dimensional Regression Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-05-22T14:56:42.490547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation bd4f8a31-4ade-47eb-8eb7-74b293f7eb1a · outbound

This paper cites an unresolved cited work.

Nash: Neural Adaptive Shrinkage for Structured High-Dimensional Regression Unresolved cited work

Reference 20

Resolution: unresolved; raw_fallback, observed 2026-05-22T14:56:42.466514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation fc94afe9-bc2f-41b9-a76c-4be225b1f6f5 · outbound

This paper cites an unresolved cited work.

Nash: Neural Adaptive Shrinkage for Structured High-Dimensional Regression Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-05-22T14:56:42.497992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 526c2ddf-b599-4788-953a-97ed8789acef · outbound

This paper cites The first inequality holds due to the Lemma above, and the second inequality is due to the definition of ELBO.

Nash: Neural Adaptive Shrinkage for Structured High-Dimensional Regression The first inequality holds due to the Lemma above, and the second inequality is due to the definition of ELBO

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T14:56:42.505688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Pith citing papers

No inbound Pith citation observations are available.