Paper Citation Record · LEDGER

Dataset Distillation via the Wasserstein Metric

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2311.18531.

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

pith.paper-citation-record.v1
2311.18531 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T05:44:25.064326Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T06:39:36.998873Z

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 1541e025-2405-4e0d-9947-ab93dc36e37c · inbound

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation cites this paper.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Dataset Distillation via the Wasserstein Metric

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-12T05:44:25.064326Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aea5dbb4-7a0d-4f15-8c3c-d3cd0c739247 · inbound

Trust-Aware Diversion for Data-Effective Distillation cites this paper.

Trust-Aware Diversion for Data-Effective Distillation Dataset Distillation via the Wasserstein Metric

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-08T20:38:25.897770Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac96e9f9-6e94-431e-9aa8-262b8d3547fc · inbound

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions cites this paper.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Dataset Distillation via the Wasserstein Metric

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-08T18:29:50.273079Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 158634ce-4b73-4f9c-b6ce-ccdb31d334f0 · inbound

Improving Noise Efficiency in Privacy-preserving Dataset Distillation cites this paper.

Improving Noise Efficiency in Privacy-preserving Dataset Distillation Dataset Distillation via the Wasserstein Metric

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T05:32:45.374982Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65927d45-4d30-4227-9d16-e306d6f0c9d0 · inbound

A Discrepancy-Based Perspective on Dataset Condensation cites this paper.

A Discrepancy-Based Perspective on Dataset Condensation Dataset Distillation via the Wasserstein Metric

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-04T17:57:54.799494Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5d8d50a-c524-4738-bdd1-1b6cc63e7380 · inbound

Wasserstein Gradient Flows for Scalable and Regularized Barycenter Computation cites this paper.

Wasserstein Gradient Flows for Scalable and Regularized Barycenter Computation Dataset Distillation via the Wasserstein Metric

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T11:29:21.212959Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 020b78ee-ccc6-4dbe-abb4-768fcd78c0c3 · inbound

DMGD: Train-Free Dataset Distillation with Semantic-Distribution Matching in Diffusion Models cites this paper.

DMGD: Train-Free Dataset Distillation with Semantic-Distribution Matching in Diffusion Models Dataset Distillation via the Wasserstein Metric

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-11T23:16:29.039399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 24c9b46c-9d4f-4a52-8e02-d57749d10064 · inbound

Distill Once, Adapt Life-Long: Exploring Dataset Distillation for Continual Test-Time Adaptation cites this paper.

Distill Once, Adapt Life-Long: Exploring Dataset Distillation for Continual Test-Time Adaptation Dataset Distillation via the Wasserstein Metric

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-07-04T03:29:29.761931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f49863b8-e8e4-4103-a15d-2f91b488b2ad · inbound

Distill Once, Adapt Life-Long: Exploring Dataset Distillation for Continual Test-Time Adaptation cites this paper.

Distill Once, Adapt Life-Long: Exploring Dataset Distillation for Continual Test-Time Adaptation Dataset Distillation via the Wasserstein Metric

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-07-01T08:45:35.518805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fda7fcf6-8be9-4ebc-be35-da367a07419c · inbound

Structural Assessment for Understanding and Guiding Dataset Distillation in Discrete Token Space cites this paper.

Structural Assessment for Understanding and Guiding Dataset Distillation in Discrete Token Space Dataset Distillation via the Wasserstein Metric

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-07-04T06:39:37.000220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 551d76a2-f819-4190-84ad-605098088a1d · inbound

Condensing Large-Scale Datasets Directly with Minimal Information Loss cites this paper.

Condensing Large-Scale Datasets Directly with Minimal Information Loss Dataset Distillation via the Wasserstein Metric

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-07-02T14:07:02.395368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.