Paper Citation Record · LEDGER

Asymptotics of Random Feature Regression Beyond the Linear Scaling Regime

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2403.08160.

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

pith.paper-citation-record.v1
2403.08160 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:12:11.889425Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T23:35:08.018234Z

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 c3d20e83-e61e-4c22-b254-495fdcce2470 · inbound

Dimension-adapted Momentum Outscales SGD cites this paper.

Dimension-adapted Momentum Outscales SGD Asymptotics of Random Feature Regression Beyond the Linear Scaling Regime

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T15:12:11.889425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 453991ba-1fa5-4b33-8a95-d959168e0e4d · inbound

Eigenvalue distribution of the Neural Tangent Kernel in the quadratic scaling cites this paper.

Eigenvalue distribution of the Neural Tangent Kernel in the quadratic scaling Asymptotics of Random Feature Regression Beyond the Linear Scaling Regime

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-05T15:24:03.434596Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 26d75a39-2e41-4959-8ae2-f14db36d765c · inbound

Statistical physics of deep learning: Optimal learning of a multi-layer perceptron near interpolation cites this paper.

Statistical physics of deep learning: Optimal learning of a multi-layer perceptron near interpolation Asymptotics of Random Feature Regression Beyond the Linear Scaling Regime

Reference 193

Resolution: unresolved; no resolver link, observed 2026-08-04T07:44:32.072046Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b149c0cb-323c-478c-bcd8-16a5e3a98eba · inbound

High-Dimensional Statistics: Reflections on Progress and Open Problems cites this paper.

High-Dimensional Statistics: Reflections on Progress and Open Problems Asymptotics of Random Feature Regression Beyond the Linear Scaling Regime

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-11T18:36:06.699682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 71184d6e-a43a-41bf-b8ca-b7840cebab0e · inbound

High-Dimensional Statistics: Reflections on Progress and Open Problems cites this paper.

High-Dimensional Statistics: Reflections on Progress and Open Problems Asymptotics of Random Feature Regression Beyond the Linear Scaling Regime

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-06-30T23:35:08.019783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e61b10a4-859c-4f49-a113-07d50c3fe6ab · inbound

Scaling Laws from Sequential Feature Recovery: A Solvable Hierarchical Model cites this paper.

Scaling Laws from Sequential Feature Recovery: A Solvable Hierarchical Model Asymptotics of Random Feature Regression Beyond the Linear Scaling Regime

Reference 108

Resolution: verified exact; arxiv_id, observed 2026-05-15T01:39:38.379265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 74275ea5-0ea7-4094-8889-120564099c76 · inbound

Feature Learning in Linear-Width Two-Layer Networks: Two vs. One Step of Gradient Descent cites this paper.

Feature Learning in Linear-Width Two-Layer Networks: Two vs. One Step of Gradient Descent Asymptotics of Random Feature Regression Beyond the Linear Scaling Regime

Reference 228

Resolution: verified exact; arxiv_id, observed 2026-05-20T01:32:56.043052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 18d32415-ff59-436f-ac0d-3700548803aa · inbound

Feature Learning in Linear-Width Two-Layer Networks: Two vs. One Step of Gradient Descent cites this paper.

Feature Learning in Linear-Width Two-Layer Networks: Two vs. One Step of Gradient Descent Asymptotics of Random Feature Regression Beyond the Linear Scaling Regime

Reference 228

Resolution: verified exact; arxiv_id, observed 2026-05-25T06:40:24.807333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.