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Paper Citation Record · LEDGER

Distributionally Robust Stochastic Optimization with Wasserstein Distance

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:1604.02199.

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

pith.paper-citation-record.v1
1604.02199 v3

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 2026-08-07T06:34:17.273281+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:44:14.436890Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

97
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 9e643465-e343-4326-9fd8-099503d3f66d · inbound

Cross-Dock Door Design under Uncertainty: A two-stage DRO-based lower- and upper-bounding scheme cites this paper.

Cross-Dock Door Design under Uncertainty: A two-stage DRO-based lower- and upper-bounding scheme Distributionally Robust Stochastic Optimization with Wasserstein Distance

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-07T11:44:14.436890Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:44:14.436890Z digest=sha256:fe4902232ae148589e4b12b5e7f6d32f3e5664bc9895c9b6a4f64a1aeb7ec642

Observation 393132e3-f13a-4eca-b079-b2abc550d714 · inbound

Gradient Flow Sampler-based Distributionally Robust Optimization cites this paper.

Gradient Flow Sampler-based Distributionally Robust Optimization Distributionally Robust Stochastic Optimization with Wasserstein Distance

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-04T07:29:19.441907Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T07:29:19.441907Z digest=sha256:22bd713af244539982ee586d067a6055cefc1de54baaa2a2be5bcadcd3bca9bf

Observation d1a91b96-5328-4c22-8bd4-49998a230a0d · inbound

Sampler-Robust Optimization under Generative Models cites this paper.

Sampler-Robust Optimization under Generative Models Distributionally Robust Stochastic Optimization with Wasserstein Distance

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-12T09:51:27.928086Z

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.

source=pdf_text observed=2026-05-07T09:02:14.883505Z digest=sha256:f7c904cfb76a2c9a262772e708e62b1be8655e86b2ee405bcc9b01da620a3e6f

Observation 4b6803fc-df1b-46a5-ab87-e1c818106dab · inbound

Ensemble Distributionally Robust Bayesian Optimisation with Continuous Context cites this paper.

Ensemble Distributionally Robust Bayesian Optimisation with Continuous Context Distributionally Robust Stochastic Optimization with Wasserstein Distance

Reference 129

Resolution
verified exact
arxiv_id, observed 2026-05-11T03:20:55.843268Z

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.

source=arxiv_source observed=2026-05-11T03:18:03.316334Z digest=sha256:3b4fc94549ead874792c2c046bf196f91c0076cf62e6811e154a0e4f5d746863

Observation 7f070b6d-0f3f-4e02-bc2f-f761dfa4e66e · inbound

Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data cites this paper.

Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data Distributionally Robust Stochastic Optimization with Wasserstein Distance

Reference 44

Resolution
verified exact
arxiv_id, observed 2026-05-13T05:57:21.757522Z

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.

source=arxiv_source observed=2026-05-13T05:56:38.042978Z digest=sha256:67c36a414ebcb3b646fd4bfaa26bd16081d2271b2aec76c7fed4a30e0ec130ef