Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Large Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency
As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2402.15926.
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 2026-08-17T06:30:58.91139+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T23:12:21.538165Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-08T13:45:15.717810Z
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 94a5df44-434e-43e0-b2ed-7ca3510b8bec · inbound
Finite Horizon Optimization: Framework and Applications cites this paper.
Finite Horizon Optimization: Framework and Applications Large Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency
Reference 1997
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T23:12:21.538165Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5f3e58ba-060e-4578-9d32-bba7b4217b6c · inbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression cites this paper.
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression Large Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency
Reference 39
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T15:46:47.740292Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bf4c4825-4567-4477-bbc6-e1781100c8b1 · inbound
Small steps no more: Global convergence of stochastic gradient bandits for arbitrary learning rates cites this paper.
Small steps no more: Global convergence of stochastic gradient bandits for arbitrary learning rates Large Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency
Reference 35
- Resolution
- verified exact
- local_arxiv, observed 2026-08-08T13:45:15.723433Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.