Paper Citation Record · LEDGER

A Geometric Approach to Problems in Optimization and Data Science

As of 19 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2504.16270.

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

pith.paper-citation-record.v1
2504.16270 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:17:20.547913Z

measured 15 of 15 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 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

15 of 15 outbound references displayed

verified exact1
verified fuzzy1
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch8

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a914bb28-572b-4f99-887a-5d3a54eabe45 · outbound

This paper cites Limit theorems for mixed-norm sequence spaces with applications to volume distribution.

A Geometric Approach to Problems in Optimization and Data Science Limit theorems for mixed-norm sequence spaces with applications to volume distribution

Reference 9

Resolution: metadata mismatch; local_arxiv, observed 2026-08-16T11:17:20.644846Z

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 230993f3-e210-4135-8179-8101b04ea7e0 · outbound

This paper cites Concentration and regularization of random graphs.

A Geometric Approach to Problems in Optimization and Data Science Concentration and regularization of random graphs

Reference 11

Resolution: metadata mismatch; local_arxiv, observed 2026-08-16T11:17:20.621167Z

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 dc598150-40e8-49ca-8aaa-ae1f3fecfcfa · outbound

This paper cites Spectral hypergraph sparsification via chaining.

A Geometric Approach to Problems in Optimization and Data Science Spectral hypergraph sparsification via chaining

Reference 12

Resolution: metadata mismatch; local_arxiv, observed 2026-08-16T11:17:20.610218Z

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 9b6ca058-9598-425f-8e0b-acd7f191630e · outbound

This paper cites Streaming Algorithms for Ellipsoidal Approximation of Convex Polytopes.

A Geometric Approach to Problems in Optimization and Data Science Streaming Algorithms for Ellipsoidal Approximation of Convex Polytopes

Reference 13

Resolution: metadata mismatch; local_arxiv, observed 2026-08-16T11:17:20.598778Z

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 2162182f-3c13-433f-9363-8d20a3e18ee0 · outbound

This paper cites [JS00] William Johnson and Gideon Schechtman.

A Geometric Approach to Problems in Optimization and Data Science [JS00] William Johnson and Gideon Schechtman

Reference 204

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:20.909514Z

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 87adcc76-5814-4a6c-a003-f00367db3c5a · outbound

This paper cites Spectral Hypergraph Sparsifiers of Nearly Linear Size.

A Geometric Approach to Problems in Optimization and Data Science Spectral Hypergraph Sparsifiers of Nearly Linear Size

Reference 1170

Resolution: metadata mismatch; local_arxiv, observed 2026-08-16T11:17:20.632572Z

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 81acf1c5-8615-4c82-81aa-b35f5f710544 · outbound

This paper cites [Dat14] Big Data.

A Geometric Approach to Problems in Optimization and Data Science [Dat14] Big Data

Reference 2003

Resolution: unresolved; no resolver link, observed 2026-08-16T11:17:20.513671Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7cc06722-b2bf-4fa8-b6c0-dcba6d4febe9 · outbound

This paper cites BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain.

A Geometric Approach to Problems in Optimization and Data Science BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-16T11:17:20.519150Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51155c3a-3587-42f4-8daf-a180bfae0de0 · outbound

This paper cites [GC23] XingGaoandYuCheng.Robustmatrixsensinginthesemi-randommodel.

A Geometric Approach to Problems in Optimization and Data Science [GC23] XingGaoandYuCheng.Robustmatrixsensinginthesemi-randommodel

Reference 2018

Resolution: metadata mismatch; raw_fallback, observed 2026-08-16T11:17:20.806684Z

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 25d932f6-1b39-425d-8e9a-d9e8a2e4625e · outbound

This paper cites A Stochastic Newton Algorithm for Distributed Convex Optimization.

A Geometric Approach to Problems in Optimization and Data Science A Stochastic Newton Algorithm for Distributed Convex Optimization

Reference 2021

Resolution: metadata mismatch; local_arxiv, observed 2026-08-16T11:17:20.878068Z

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 4f83eca0-1b01-4a7c-afce-af30327d893d · outbound

This paper cites Improved Iteration Complexities for Overconstrained $p$-Norm Regression.

A Geometric Approach to Problems in Optimization and Data Science Improved Iteration Complexities for Overconstrained $p$-Norm Regression

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-16T11:17:20.522094Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15beeb72-97be-47fa-8c66-41a48a005c0c · outbound

This paper cites Algorithms approaching the threshold for semi-random planted clique.

A Geometric Approach to Problems in Optimization and Data Science Algorithms approaching the threshold for semi-random planted clique

Reference 2023

Resolution: metadata mismatch; local_arxiv, observed 2026-08-16T11:17:20.890617Z

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 80e64c2a-5702-402c-9066-460ebc3aa026 · outbound

This paper cites Near-Optimal Streaming Ellipsoidal Rounding for General Convex Polytopes.

A Geometric Approach to Problems in Optimization and Data Science Near-Optimal Streaming Ellipsoidal Rounding for General Convex Polytopes

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-16T11:17:20.544162Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66ea5029-a750-4297-81be-3a8cc0b07d71 · outbound

This paper cites Agnostic Federated Learning.

A Geometric Approach to Problems in Optimization and Data Science Agnostic Federated Learning

Reference 4625

Resolution: unresolved; no resolver link, observed 2026-08-16T11:17:20.547913Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6eeda20-f524-4694-a709-149b4e9506bc · outbound

This paper cites Strong Data Augmentation Sanitizes Poisoning and Backdoor Attacks Without an Accuracy Tradeoff.

A Geometric Approach to Problems in Optimization and Data Science Strong Data Augmentation Sanitizes Poisoning and Backdoor Attacks Without an Accuracy Tradeoff

Reference 6774

Resolution: verified exact; local_arxiv, observed 2026-08-16T11:17:20.901132Z

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.

Pith citing papers

No inbound Pith citation observations are available.