Paper Citation Record · LEDGER

On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:1902.04811.

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

pith.paper-citation-record.v1
1902.04811 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T13:09:59.968238Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T07:36:45.174590Z

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 1dd7419e-d602-486b-91bf-1021caae4a54 · inbound

Distributed Learning in Non-Convex Environments -- Part I: Agreement at a Linear Rate cites this paper.

Distributed Learning in Non-Convex Environments -- Part I: Agreement at a Linear Rate On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-25T10:25:38.607286Z

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.

Observation 2f123d49-9a9a-4cf4-9cc6-d8625eafd6d8 · inbound

Distributed Learning in Non-Convex Environments -- Part II: Polynomial Escape from Saddle-Points cites this paper.

Distributed Learning in Non-Convex Environments -- Part II: Polynomial Escape from Saddle-Points On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-25T09:55:36.558456Z

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.

Observation 28ebbe59-f608-4d49-b083-eabef028782a · inbound

Learning from Limited and Imperfect Data cites this paper.

Learning from Limited and Imperfect Data On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points

Reference 123

Resolution: unresolved; no resolver link, observed 2026-08-06T13:09:59.968238Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 251df33f-3283-40a0-903c-faf888aaccea · inbound

Globally aware optimization with resurgence cites this paper.

Globally aware optimization with resurgence On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T12:46:30.602557Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 824e1e3a-0fd3-407f-acec-4679c98c48a6 · inbound

Convergence of difference inclusions via a diameter criterion cites this paper.

Convergence of difference inclusions via a diameter criterion On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points

Reference 291

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T02:23:31.781196Z

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.

Observation c25847ea-ec12-4c43-ab3d-d04b28236c62 · inbound

Sharp First-Order Lower Bounds for Higher-Order Smooth Nonconvex Optimization cites this paper.

Sharp First-Order Lower Bounds for Higher-Order Smooth Nonconvex Optimization On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-07-02T07:36:45.176067Z

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.