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Paper Citation Record · LEDGER

Lower Bounds for Non-Convex Stochastic Optimization

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1912.02365.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1912.02365 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:27:18.386639Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-21T21:22:36.985126Z

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 d1ac6a8a-d3ef-4a6f-a8a6-2f7fe30aeab6 · inbound

Training Deep Learning Models with Norm-Constrained LMOs cites this paper.

Training Deep Learning Models with Norm-Constrained LMOs Lower Bounds for Non-Convex Stochastic Optimization

Reference 154

Resolution
verified exact
arxiv_id, observed 2026-05-21T21:22:36.987752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-21T21:22:36.870292Z digest=sha256:9fdd8deb419854869568be9153c19e34a163b404b7a1a77dc15929fe803267a0

Observation e9e687f1-298e-4ce2-8ed5-7dc8189b02e5 · inbound

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness cites this paper.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Lower Bounds for Non-Convex Stochastic Optimization

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-06T21:27:18.386639Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:27:18.386639Z digest=sha256:0146c6c8926497be641de99f398951a8149e10c47f3769a91db679f6d4e0a992