Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Training individually fair ML models with Sensitive Subspace Robustness
As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1907.00020.
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 2026-08-19T06:32:44.657259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T10:24:04.868995Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-18T18:01:43.743068Z
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 a5319e8e-840d-4f44-b0d1-f23ebe0f3c15 · inbound
Local Statistical Parity for the Estimation of Fair Decision Trees cites this paper.
Local Statistical Parity for the Estimation of Fair Decision Trees Training individually fair ML models with Sensitive Subspace Robustness
Reference 27
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T10:24:04.868995Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 37a7f195-48fc-424e-b955-21d4fbacf2a0 · inbound
Concolic Testing on Individual Fairness of Neural Network Models cites this paper.
Concolic Testing on Individual Fairness of Neural Network Models Training individually fair ML models with Sensitive Subspace Robustness
Reference 24
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T18:01:43.746097Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation f21fed9e-f44f-4667-9d5b-bb2cba3d3d9b · inbound
Toward Individual Fairness Without Centralized Data: Selective Counterfactual Consistency for Vertical Federated Learning cites this paper.
Toward Individual Fairness Without Centralized Data: Selective Counterfactual Consistency for Vertical Federated Learning Training individually fair ML models with Sensitive Subspace Robustness
Reference 56
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T04:20:59.065722Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.