Paper Citation Record · LEDGER

Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2101.08030.

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

pith.paper-citation-record.v1
2101.08030 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 2026-08-08T06:32:00.761636+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:37:04.893327Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

14
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation fb8f0299-0ab6-4abf-838a-2813a788aa34 · inbound

TabICL: A Tabular Foundation Model for In-Context Learning on Large Data cites this paper.

TabICL: A Tabular Foundation Model for In-Context Learning on Large Data Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-05-20T13:35:02.210605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 56346855-ecb4-4a9a-a2ad-ef942cfac08e · inbound

MUSE: Model-Agnostic Tabular Watermarking via Multi-Sample Selection cites this paper.

MUSE: Model-Agnostic Tabular Watermarking via Multi-Sample Selection Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T12:37:04.893327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f59bd82c-86a8-4d6e-98e8-ffb13362be79 · inbound

Shapes are not enough: CONSERVAttack and its use for finding vulnerabilities and uncertainties in machine learning applications cites this paper.

Shapes are not enough: CONSERVAttack and its use for finding vulnerabilities and uncertainties in machine learning applications Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T11:25:31.137128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0435ace3-8467-411a-bea0-fca9c8e0feeb · inbound

When AI Meets Wall Street: A Survey on Trustworthy AI in Fintech cites this paper.

When AI Meets Wall Street: A Survey on Trustworthy AI in Fintech Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-06-29T14:43:31.563202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.