Paper Citation Record · LEDGER

Universality of max-margin classifiers

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2310.00176.

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

pith.paper-citation-record.v1
2310.00176 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 2026-08-09T06:31:02.800959+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T14:37:13.470662Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T23:35:08.048157Z

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 5e6d5f04-4fcd-452a-809b-05fd50ccb850 · inbound

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation cites this paper.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Universality of max-margin classifiers

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-08T14:37:13.470662Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa62b628-cf07-43a0-8064-51104de81fb2 · inbound

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing cites this paper.

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Universality of max-margin classifiers

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-06T22:55:23.961460Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4643ca2-6c66-435b-a231-cbb4cad00362 · inbound

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

High-Dimensional Statistics: Reflections on Progress and Open Problems Universality of max-margin classifiers

Reference 72

Resolution: verified exact; arxiv_id, observed 2026-05-11T18:36:06.458335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4a05e45c-a1ce-4653-b561-e2a0bac49e81 · inbound

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

High-Dimensional Statistics: Reflections on Progress and Open Problems Universality of max-margin classifiers

Reference 72

Resolution: verified exact; arxiv_id, observed 2026-06-30T23:35:08.049788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.