Paper Citation Record · LEDGER

Projected support points: a new method for high-dimensional data reduction

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1708.06897.

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

pith.paper-citation-record.v1
1708.06897 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 2026-08-06T18:11:17.427012Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-02T02:46:28.729052Z

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 3e18f1e7-a737-451f-aba2-39e826364240 · inbound

Robust designs for Gaussian process emulation of computer experiments cites this paper.

Robust designs for Gaussian process emulation of computer experiments Projected support points: a new method for high-dimensional data reduction

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T18:11:17.427012Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82d6e583-6829-4531-80c7-ef88fbb3ed5e · inbound

Weighted Support Points from Random Measures: An Interpretable Alternative for Generative Modeling cites this paper.

Weighted Support Points from Random Measures: An Interpretable Alternative for Generative Modeling Projected support points: a new method for high-dimensional data reduction

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T14:33:19.829874Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68b004c4-54b9-40d8-af58-856e72f40a9b · inbound

BAMIFun: Bayesian Multiple Imputation for Functional Data cites this paper.

BAMIFun: Bayesian Multiple Imputation for Functional Data Projected support points: a new method for high-dimensional data reduction

Reference 166

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:45:58.194567Z

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 899d7db4-5860-4ccd-b9c1-af97a624d536 · inbound

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials cites this paper.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Projected support points: a new method for high-dimensional data reduction

Reference 61

Resolution: metadata mismatch; local_arxiv, observed 2026-07-02T02:46:28.731608Z

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.