Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
LoAdaBoost: loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data
As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1811.12629.
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 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-14T13:25:35.801273Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-24T21:34:58.547966Z
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 c601714e-4968-413d-8365-10505ad4d388 · inbound
Multi-hop Federated Private Data Augmentation with Sample Compression cites this paper.
Multi-hop Federated Private Data Augmentation with Sample Compression LoAdaBoost: loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data
Reference 3
- Resolution
- verified exact
- arxiv_id, observed 2026-05-24T21:34:58.551434Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 1d44afa3-ab77-46f7-9026-e5eb289deef1 · inbound
Two-stage Federated Phenotyping and Patient Representation Learning cites this paper.
Two-stage Federated Phenotyping and Patient Representation Learning LoAdaBoost: loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T13:25:35.801273Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation eac7a29c-71a8-4e82-8ea9-459b6bd5034c · inbound
Federated Learning: Challenges, Methods, and Future Directions cites this paper.
Federated Learning: Challenges, Methods, and Future Directions LoAdaBoost: loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data
Reference 52
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T11:57:36.135995Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.