Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Online Convex Optimization Perspective for Learning from Dynamically Revealed Preferences
As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2008.10460.
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 2026-08-23T06:30:58.430688+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T15:55:50.973632Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-16T08:57:38.622345Z
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 ace538cb-9446-43fa-b9e7-5a4a56490dc3 · inbound
Revisiting Online Learning Approach to Inverse Linear Optimization: A Fenchel$-$Young Loss Perspective and Gap-Dependent Regret Analysis cites this paper.
Revisiting Online Learning Approach to Inverse Linear Optimization: A Fenchel$-$Young Loss Perspective and Gap-Dependent Regret Analysis Online Convex Optimization Perspective for Learning from Dynamically Revealed Preferences
Reference 134
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T15:55:50.973632Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 13dd1374-54f3-402e-8493-46f73876a2e6 · inbound
Finite and Corruption-Robust Regret Bounds in Online Inverse Linear Optimization under M-Convex Action Sets cites this paper.
Finite and Corruption-Robust Regret Bounds in Online Inverse Linear Optimization under M-Convex Action Sets Online Convex Optimization Perspective for Learning from Dynamically Revealed Preferences
Reference 11
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T08:57:38.624477Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.