Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Optimizer's Information Criterion: Dissecting and Correcting Bias in Data-Driven Optimization
As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2306.10081.
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 2026-08-15T15:19:36.464441Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-23T02:25:19.305168Z
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 19f79432-2d74-4c87-9a6a-1ba6d6c3d465 · inbound
Risk-averse Decision Making with Contextual Information: Model, Sample Average Approximation, and Kernelization cites this paper.
Risk-averse Decision Making with Contextual Information: Model, Sample Average Approximation, and Kernelization Optimizer's Information Criterion: Dissecting and Correcting Bias in Data-Driven Optimization
Reference 32
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T02:25:19.308138Z
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 f8289f45-bee2-48f2-9b4d-5a45302892e0 · inbound
DRO: A Python Library for Distributionally Robust Optimization in Machine Learning cites this paper.
DRO: A Python Library for Distributionally Robust Optimization in Machine Learning Optimizer's Information Criterion: Dissecting and Correcting Bias in Data-Driven Optimization
Reference 2013
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:45:45.683237Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 628af481-be7b-483a-9997-939611d299b7 · inbound
When May a Model Replace the Experiment? Audits, Licenses, and the Price of Trust in Surrogate-Driven Design cites this paper.
When May a Model Replace the Experiment? Audits, Licenses, and the Price of Trust in Surrogate-Driven Design Optimizer's Information Criterion: Dissecting and Correcting Bias in Data-Driven Optimization
Reference 25
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T15:19:36.464441Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.