Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
On the Complexity of Learning Sparse Functions with Statistical and Gradient Queries
As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2407.05622.
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 2026-08-10T14:57:03.350210Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-08T15:34:17.012725Z
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 93e9b7d6-1627-484d-802a-f7d925620940 · inbound
Decision Making in Changing Environments: Robustness, Query-Based Learning, and Differential Privacy cites this paper.
Decision Making in Changing Environments: Robustness, Query-Based Learning, and Differential Privacy On the Complexity of Learning Sparse Functions with Statistical and Gradient Queries
Reference 58
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T14:57:03.350210Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d7219710-d2b4-403d-be77-1e150fc454e7 · inbound
Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions cites this paper.
Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions On the Complexity of Learning Sparse Functions with Statistical and Gradient Queries
Reference 31
- Resolution
- verified exact
- local_arxiv, observed 2026-08-08T15:34:17.020132Z
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 085a2522-f415-4d3f-a47d-a0ea9f0e7fa8 · inbound
The Multiscale Single-Index Model: A Stylized Model for Hierarchical Feature Learning cites this paper.
The Multiscale Single-Index Model: A Stylized Model for Hierarchical Feature Learning On the Complexity of Learning Sparse Functions with Statistical and Gradient Queries
Reference 35
- Resolution
- unresolved
- no resolver link, observed 2026-07-12T03:07:54.001891Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.