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Paper Citation Record · LEDGER

Weakly-Convex Concave Min-Max Optimization: Provable Algorithms and Applications in Machine Learning

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:1810.02060.

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

pith.paper-citation-record.v1
1810.02060 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T15:09:34.350523Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-24T02:43:47.468272Z

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 8a01cffa-3ca9-41e6-a446-f73592b41529 · inbound

Quadratically Regularized Subgradient Methods for Weakly Convex Optimization with Weakly Convex Constraints cites this paper.

Quadratically Regularized Subgradient Methods for Weakly Convex Optimization with Weakly Convex Constraints Weakly-Convex Concave Min-Max Optimization: Provable Algorithms and Applications in Machine Learning

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-14T15:09:34.350523Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T15:09:34.350523Z digest=sha256:60bc6e1ab875ed391ba7600658606cfbbb087d90dc542aabe1abf460b0a21d56

Observation fb3b905f-bf2c-4fe3-b4ef-70bfc2aafa98 · inbound

Stochastic First-order Methods for Convex and Nonconvex Functional Constrained Optimization cites this paper.

Stochastic First-order Methods for Convex and Nonconvex Functional Constrained Optimization Weakly-Convex Concave Min-Max Optimization: Provable Algorithms and Applications in Machine Learning

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-14T14:45:51.790614Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T14:45:51.790614Z digest=sha256:65290aa69b7a6a3f307df7f3b9596891ac038768b1788844e42738e48f64ce0a

Observation cdfeb55e-bfeb-4559-8e98-3dc9d8744051 · inbound

Stochastic Optimization for Non-convex Inf-Projection Problems cites this paper.

Stochastic Optimization for Non-convex Inf-Projection Problems Weakly-Convex Concave Min-Max Optimization: Provable Algorithms and Applications in Machine Learning

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-14T11:05:32.330364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T11:05:32.330364Z digest=sha256:0714c4761f432b2f706696e6e00c4e3f4c2b9d96d2783ee4a3f2686d2dc3a216

Observation 7a8f2219-79ec-4ae4-aa29-a825a400b4b0 · inbound

Stochastic AUC Maximization with Deep Neural Networks cites this paper.

Stochastic AUC Maximization with Deep Neural Networks Weakly-Convex Concave Min-Max Optimization: Provable Algorithms and Applications in Machine Learning

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-14T10:41:34.777456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T10:41:34.777456Z digest=sha256:a1e7d0e494f3d1de9f45bef7ec998ed4ad0ef68488cf8df59160ada40473a6f6

Observation 8769fc15-422e-4c83-92fc-681f30c119b2 · inbound

A Stochastic GDA Method With Backtracking For Solving Nonconvex Concave Minimax Problems cites this paper.

A Stochastic GDA Method With Backtracking For Solving Nonconvex Concave Minimax Problems Weakly-Convex Concave Min-Max Optimization: Provable Algorithms and Applications in Machine Learning

Reference 54

Resolution
verified exact
arxiv_id, observed 2026-05-24T02:43:47.470859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-24T02:40:21.808440Z digest=sha256:62052bb536f30398f066735b0ebeeb9d07fda0792cb0833415986dbc556437e4