Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
A stochastic smoothing framework for nonconvex-nonconcave minEmax problems with applications to Wasserstein distributionally robust optimization
As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2502.17602.
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 2026-08-11T06:34:44.6726+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-09T21:05:04.825607Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T02:09:22.309939Z
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 49efc588-6368-4840-af66-d3d160965aea · inbound
Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems cites this paper.
Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems A stochastic smoothing framework for nonconvex-nonconcave minEmax problems with applications to Wasserstein distributionally robust optimization
Reference 84
- Resolution
- verified exact
- arxiv_id, observed 2026-07-17T01:20:34.521445Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 71f574bf-ad0b-4b29-a6df-0d3e3c34ecd2 · inbound
A single-loop SPIDER-type stochastic subgradient method for expectation-constrained nonconvex nonsmooth optimization cites this paper.
A single-loop SPIDER-type stochastic subgradient method for expectation-constrained nonconvex nonsmooth optimization A stochastic smoothing framework for nonconvex-nonconcave minEmax problems with applications to Wasserstein distributionally robust optimization
Reference 41
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T21:05:04.825607Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1a650219-28e5-4057-882d-c1fa441de7e0 · inbound
Unregularized limit of stochastic gradient method for Wasserstein distributionally robust optimization cites this paper.
Unregularized limit of stochastic gradient method for Wasserstein distributionally robust optimization A stochastic smoothing framework for nonconvex-nonconcave minEmax problems with applications to Wasserstein distributionally robust optimization
Reference 30
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:40:36.797712Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bc98d703-1511-4ad1-ac74-c0fbda8dae16 · inbound
First-Order Methods for Solving Convex (Strongly) Concave Minimax Problems with Functional Constraints cites this paper.
First-Order Methods for Solving Convex (Strongly) Concave Minimax Problems with Functional Constraints A stochastic smoothing framework for nonconvex-nonconcave minEmax problems with applications to Wasserstein distributionally robust optimization
Reference 3
- Resolution
- verified exact
- arxiv_id, observed 2026-07-17T01:20:34.521445Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.