Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2211.06530.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T21:09:31.301910Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-30T12:34:39.086116Z
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 6c7ffa78-b475-4c0d-9736-3a608c045459 · inbound
On Design Principles for Private Adaptive Optimizers cites this paper.
On Design Principles for Private Adaptive Optimizers Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T21:09:31.301910Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1d799564-5a30-493a-9e5f-d1ce81345c84 · inbound
Securing Private Federated Learning in a Malicious Setting: A Scalable TEE-Based Approach with Client Auditing cites this paper.
Securing Private Federated Learning in a Malicious Setting: A Scalable TEE-Based Approach with Client Auditing Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T20:27:30.162705Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0ac29956-7884-49b0-83fb-e8e6b45ccaab · inbound
Efficient DP-SGD for LLMs with Randomized Clipping cites this paper.
Efficient DP-SGD for LLMs with Randomized Clipping Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning
Reference 17
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T12:34:39.087834Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.