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Paper Citation Record · LEDGER

Selective Pre-training for Private Fine-tuning

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2305.13865.

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

pith.paper-citation-record.v1
2305.13865 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T19:09:04.148957Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T04:44:03.163713Z

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 21940087-afa8-4232-bb91-27996c17f5ad · inbound

Textbooks Are All You Need cites this paper.

Textbooks Are All You Need Selective Pre-training for Private Fine-tuning

Reference 30

Resolution
verified exact
arxiv_id, observed 2026-05-13T04:44:03.166243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-13T04:44:03.148223Z digest=sha256:38bfaf702b0e0b3bd7be54e6e1b209eb394d4c68dd2537745e17b82a7d012070

Observation a7aba2a3-2edb-45b0-97ac-c4963f8f7a81 · inbound

Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model cites this paper.

Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Selective Pre-training for Private Fine-tuning

Reference 67

Resolution
unresolved
no resolver link, observed 2026-08-08T19:09:04.148957Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T19:09:04.148957Z digest=sha256:f8a3b989a9c29dec7a0f17c971f0b9f443915ef28335df8c252803a55f9af6a2

Observation 8f718141-f159-4e2e-969d-0d903631a3ac · inbound

Public Data Assisted Differentially Private In-Context Learning cites this paper.

Public Data Assisted Differentially Private In-Context Learning Selective Pre-training for Private Fine-tuning

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-04T17:28:50.883396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T17:28:50.883396Z digest=sha256:676b28d65b08e2498855e6a87533e1dfbcff997e7a604e2ac863aa6f31009bbf