Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Why You Should Not Trust Interpretations in Machine Learning: Adversarial Attacks on Partial Dependence Plots
As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2404.18702.
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 2026-08-15T22:29:34.874456Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-23T20:23:24.635709Z
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 b0ca9e8c-7b73-4173-92e4-d36ccf2d5ebd · inbound
Explainable AI needs formalization cites this paper.
Explainable AI needs formalization Why You Should Not Trust Interpretations in Machine Learning: Adversarial Attacks on Partial Dependence Plots
Reference 13
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T20:23:24.638927Z
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 5f9c9733-0550-4842-8bdf-03b17807c3bd · inbound
From Point to probabilistic gradient boosting for claim frequency and severity prediction cites this paper.
From Point to probabilistic gradient boosting for claim frequency and severity prediction Why You Should Not Trust Interpretations in Machine Learning: Adversarial Attacks on Partial Dependence Plots
Reference 45
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T11:51:46.108890Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 95fba2d7-1c2e-48e4-8bd0-cb42daa910b5 · inbound
Explainable AI the Latest Advancements and New Trends cites this paper.
Explainable AI the Latest Advancements and New Trends Why You Should Not Trust Interpretations in Machine Learning: Adversarial Attacks on Partial Dependence Plots
Reference 99
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T22:29:34.874456Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 37cccfde-2099-4236-9bb9-796761609d5a · inbound
Unveil Sources of Uncertainty: Feature Contribution to Conformal Prediction Intervals cites this paper.
Unveil Sources of Uncertainty: Feature Contribution to Conformal Prediction Intervals Why You Should Not Trust Interpretations in Machine Learning: Adversarial Attacks on Partial Dependence Plots
Reference 74
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:27:39.928573Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a7675f1c-fc10-49f2-98ff-4beb1e4e0abd · inbound
Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models cites this paper.
Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models Why You Should Not Trust Interpretations in Machine Learning: Adversarial Attacks on Partial Dependence Plots
Reference 27
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T00:32:56.074741Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.