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Paper Citation Record · LEDGER

Learning to be adversarially robust and differentially private

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2201.02265.

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

pith.paper-citation-record.v1
2201.02265 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:28:27.355483Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T17:33:45.373270Z

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 0e7f07a2-8904-4ddc-9cd6-e90065e347ac · inbound

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization cites this paper.

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization Learning to be adversarially robust and differentially private

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-11T20:28:27.355483Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:28:27.355483Z digest=sha256:0d6fa0ed8145ce1f2644927d65915434b054c2ae4cafd71b2ebc6f27aeb189ce

Observation fa651555-97f0-4cb9-a31c-8ea3fee11543 · inbound

Landseer: Exploring the Machine Learning Defense Landscape cites this paper.

Landseer: Exploring the Machine Learning Defense Landscape Learning to be adversarially robust and differentially private

Reference 43

Resolution
verified exact
arxiv_id, observed 2026-06-29T17:33:45.374744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-06-29T17:27:29.241219Z digest=sha256:6e5475bf73b6d87c2f8191d8df4802996429457f2fe9151efb93ebaada02f3b3