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Paper Citation Record · LEDGER

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

As of 23 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 2026-08-22T06:32:14.747728+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=sha256:5e6e513f56a05d5fda70a82a27da6e1f053ea38f7a3dd16dc74d25ec4cebf899

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=sha256:9c6fd843efaa5ade814531483d8ce1fa058443ce4841d99051883150b5574eb6

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=sha256:7b9886e7f967cc0899542308adeea6a644457ea334570ab39dd4427bca673afc

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:d19195271f6dd2048058b1e6c21fdd7b99b773980c1ad6ea10aeab4426148e19

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-24T02:40:21.808440Z digest=sha256:793960d5f7a371dd531691ee843c4ea7ee31cc07f5b6a1135ec07d73538136d8