Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Simple and optimal high-probability bounds for strongly-convex stochastic gradient descent
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1909.00843.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-06-28T23:11:02.699220Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-28T23:12:46.677051Z
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 3e8e37cc-b055-4c9b-ba69-fcd82ed2ac51 · inbound
Sample Complexity of Stochastic Optimization with Integer Variables cites this paper.
Sample Complexity of Stochastic Optimization with Integer Variables Simple and optimal high-probability bounds for strongly-convex stochastic gradient descent
Reference 76
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T03:50:58.058694Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation c1e50c46-90fe-4333-b432-9c91bad7107f · inbound
Concentration of General Stochastic Approximation Under Heavy-Tailed Markovian Noise cites this paper.
Concentration of General Stochastic Approximation Under Heavy-Tailed Markovian Noise Simple and optimal high-probability bounds for strongly-convex stochastic gradient descent
Reference 216
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T02:29:25.202488Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 39043d7d-7c7d-4da8-9ad5-12ff19af82ff · inbound
Non-Asymptotic Convergence of Stochastic Iterative Algorithms: A Lyapunov Framework cites this paper.
Non-Asymptotic Convergence of Stochastic Iterative Algorithms: A Lyapunov Framework Simple and optimal high-probability bounds for strongly-convex stochastic gradient descent
Reference 57
- Resolution
- verified exact
- arxiv_id, observed 2026-06-28T23:12:46.678577Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.