Paper Citation Record · LEDGER

Standard Gaussian Process is All You Need for High-Dimensional Bayesian Optimization

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2402.02746.

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

pith.paper-citation-record.v1
2402.02746 v5

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 2026-08-09T06:31:02.800959+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-13T20:27:45.196743Z

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

2
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 0a873be3-3dc3-4576-89c5-22b9734cbeff · inbound

Understanding High-Dimensional Bayesian Optimization cites this paper.

Understanding High-Dimensional Bayesian Optimization Standard Gaussian Process is All You Need for High-Dimensional Bayesian Optimization

Reference 56

Resolution: verified exact; arxiv_id, observed 2026-05-23T03:32:28.427705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3624f8ed-31f0-4ba6-bcef-aa8565db7bde · inbound

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data cites this paper.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Standard Gaussian Process is All You Need for High-Dimensional Bayesian Optimization

Reference 53

Resolution: unresolved; no resolver link, observed 2026-07-13T20:27:45.196743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 937346b5-dcac-4eb1-a0ae-0cb427775c26 · inbound

Active Learning for Gaussian Process Regression Under Self-Induced Boltzmann Weights cites this paper.

Active Learning for Gaussian Process Regression Under Self-Induced Boltzmann Weights Standard Gaussian Process is All You Need for High-Dimensional Bayesian Optimization

Reference 78

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T06:21:25.311361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 31a3d58b-d8a1-4e0c-90ed-8579627170a3 · inbound

Constrained Bayesian Optimisation with Multiple Information Sources cites this paper.

Constrained Bayesian Optimisation with Multiple Information Sources Standard Gaussian Process is All You Need for High-Dimensional Bayesian Optimization

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-07-02T15:57:06.283257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.