Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Seeded Binary Segmentation: A general methodology for fast and optimal change point detection
As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2002.06633.
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 2026-08-22T06:32:14.747728+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-12T07:46:03.995262Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-28T18:02:26.911118Z
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 82a14830-3d1d-4a34-b813-f0fea7f12dd3 · inbound
Seeded intervals and noise level estimation in change point detection: A discussion of Fryzlewicz (2020) cites this paper.
Seeded intervals and noise level estimation in change point detection: A discussion of Fryzlewicz (2020) Seeded Binary Segmentation: A general methodology for fast and optimal change point detection
Reference 8
- Resolution
- verified exact
- arxiv_id, observed 2026-05-24T14:34:34.877786Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation ff5a70b6-dcea-4e73-bbd1-1e9c22cf4c43 · inbound
Hypothesis Testing for a Functional Parameter via Self-normalization cites this paper.
Hypothesis Testing for a Functional Parameter via Self-normalization Seeded Binary Segmentation: A general methodology for fast and optimal change point detection
Reference 13
- Resolution
- verified exact
- arxiv_id, observed 2026-06-28T18:02:26.912848Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation f6260bfa-3538-42fb-b9c1-7195097a3f98 · inbound
Contaminated Multi-task Learning with Heterogeneity: Fundamental Limits and Optimal Algorithms cites this paper.
Contaminated Multi-task Learning with Heterogeneity: Fundamental Limits and Optimal Algorithms Seeded Binary Segmentation: A general methodology for fast and optimal change point detection
Reference 233
- Resolution
- unresolved
- no resolver link, observed 2026-07-12T07:46:03.995262Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.