Paper Citation Record · LEDGER

Enhancing Distributional Robustness in Principal Component Analysis by Wasserstein Distances

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2503.02494.

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

pith.paper-citation-record.v1
2503.02494 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T00:04:15.698058Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T07:57:44.400029Z

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 b944586f-054d-4cb5-b600-ce915d6a1e4f · inbound

An inexact variable metric proximal linearization method for composite optimization on manifolds cites this paper.

An inexact variable metric proximal linearization method for composite optimization on manifolds Enhancing Distributional Robustness in Principal Component Analysis by Wasserstein Distances

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-18T23:11:53.594171Z

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 22f78193-5692-489b-b0d9-9377e4dac1a1 · inbound

A Support-Set Algorithm for Optimization Problems with Nonnegative and Orthogonal Constraints cites this paper.

A Support-Set Algorithm for Optimization Problems with Nonnegative and Orthogonal Constraints Enhancing Distributional Robustness in Principal Component Analysis by Wasserstein Distances

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T00:04:15.698058Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4f0e8e1b-f108-4e99-8704-ca088e5e462c · inbound

An Adaptive Smoothing Algorithm for Non-Lipschitz Optimization on Manifolds with Complexity Guarantees cites this paper.

An Adaptive Smoothing Algorithm for Non-Lipschitz Optimization on Manifolds with Complexity Guarantees Enhancing Distributional Robustness in Principal Component Analysis by Wasserstein Distances

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-11T11:56:23.076631Z

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 fe670a3b-879f-4c76-aba2-8d0fc5231410 · inbound

Distributionally Robust PCA with Data-Adaptive Wasserstein Geometry cites this paper.

Distributionally Robust PCA with Data-Adaptive Wasserstein Geometry Enhancing Distributional Robustness in Principal Component Analysis by Wasserstein Distances

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-07-03T07:57:44.401710Z

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.