Paper Citation Record · LEDGER

A Precise Performance Analysis of Learning with Random Features

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2008.11904.

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

pith.paper-citation-record.v1
2008.11904 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:53:45.532651Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T01:39:38.335684Z

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 d18ad00b-b283-4c7f-adb1-36d1631a06ec · inbound

Information-theoretic reduction of deep neural networks to linear models in the overparametrized proportional regime cites this paper.

Information-theoretic reduction of deep neural networks to linear models in the overparametrized proportional regime A Precise Performance Analysis of Learning with Random Features

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T23:53:45.532651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d2fe77bd-ae7d-4f94-8e33-d36bf32ef225 · inbound

Dataset Distillation Efficiently Encodes Low-Dimensional Representations from Gradient-Based Learning of Non-Linear Tasks cites this paper.

Dataset Distillation Efficiently Encodes Low-Dimensional Representations from Gradient-Based Learning of Non-Linear Tasks A Precise Performance Analysis of Learning with Random Features

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-14T20:55:11.027028Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ac0783a-83e9-443e-a02a-a359dca114de · inbound

Characterizing the Generalization Error of Random Feature Regression with Arbitrary Data-Augmentation cites this paper.

Characterizing the Generalization Error of Random Feature Regression with Arbitrary Data-Augmentation A Precise Performance Analysis of Learning with Random Features

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-12T05:51:25.406503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 330d99e1-eaba-4968-9435-22cab4b4facf · inbound

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

Scaling Laws from Sequential Feature Recovery: A Solvable Hierarchical Model A Precise Performance Analysis of Learning with Random Features

Reference 141

Resolution: verified exact; arxiv_id, observed 2026-05-15T01:39:38.337771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.