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Paper Citation Record · LEDGER

Covariate Shift in High-Dimensional Random Feature Regression

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2111.08234.

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

pith.paper-citation-record.v1
2111.08234 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T17:07:23.322960Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T13:12:18.790583Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation ae85031c-f9d2-4be3-b2b9-cc8174e4cfe6 · inbound

Loss-to-Loss Prediction: Scaling Laws for All Datasets cites this paper.

Loss-to-Loss Prediction: Scaling Laws for All Datasets Covariate Shift in High-Dimensional Random Feature Regression

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-12T17:07:23.322960Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T17:07:23.322960Z digest=sha256:6b9a13d7a2e0aac066826f89c37422633d6b01e7110c9f0fc0432b02513579c6

Observation e266834e-4352-4d5f-920c-6e940ee66386 · inbound

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators cites this paper.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators Covariate Shift in High-Dimensional Random Feature Regression

Reference 60

Resolution
verified exact
local_arxiv, observed 2026-08-07T13:12:18.856399Z

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=2026-08-07T13:12:14.915925Z digest=sha256:fb46641113e54ed5fb0a5afe92523b3b532979fd741d6e255a134ef465769511