Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Choosing Public Datasets for Private Machine Learning via Gradient Subspace Distance
As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2303.01256.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T11:55:18.084536Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-21T23:50:47.506253Z
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 a7e9dd62-b028-48d2-b709-c5bd8be95eba · inbound
Do You Really Need Public Data? Surrogate Public Data for Differential Privacy on Tabular Data cites this paper.
Do You Really Need Public Data? Surrogate Public Data for Differential Privacy on Tabular Data Choosing Public Datasets for Private Machine Learning via Gradient Subspace Distance
Reference 49
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:55:18.084536Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5f0e74ae-f963-4dd2-99ab-eedae359305b · inbound
Memory-Efficient Differentially Private Training with Gradient Random Projection cites this paper.
Memory-Efficient Differentially Private Training with Gradient Random Projection Choosing Public Datasets for Private Machine Learning via Gradient Subspace Distance
Reference 10
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T23:50:47.509207Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation d686d55a-ff0e-49bd-a37d-867ad5936a23 · inbound
Balancing Utility and Privacy: Dynamically Private SGD with Random Projection cites this paper.
Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Choosing Public Datasets for Private Machine Learning via Gradient Subspace Distance
Reference 25
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T19:11:56.986690Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.