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Paper Citation Record · LEDGER

Bayesian Optimization in Linear Time

As of 20 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2605.00237.

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

pith.paper-citation-record.v1
2605.00237 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-09T20:20:43.894093Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

31 of 31 outbound references displayed

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  • verified fuzzy26
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f04610bf-e698-4a6e-9a80-89c2c7477f48 · outbound

This paper cites Jiang, Samuel Daulton, Benjamin Letham, Andrew Gordon Wilson, and Ey- tan Bakshy.

Bayesian Optimization in Linear Time Jiang, Samuel Daulton, Benjamin Letham, Andrew Gordon Wilson, and Ey- tan Bakshy

Reference 1

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation b3ba2d56-6273-45eb-992b-839c9f571bef · outbound

This paper cites R package version 1.0.4.

Bayesian Optimization in Linear Time R package version 1.0.4

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation ac38a4a7-f6a2-42fa-a51c-ac031de526a3 · outbound

This paper cites an unresolved cited work.

Bayesian Optimization in Linear Time Unresolved cited work

Reference 3

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 6bba2cbc-e09b-42c6-b5bd-76d1fe666b2d · outbound

This paper cites Standard particle swarm optimisation.HAL preprint hal-00764996.

Bayesian Optimization in Linear Time Standard particle swarm optimisation.HAL preprint hal-00764996

Reference 4

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 856f232d-a7a0-4bfd-a83f-b4663890dbae · outbound

This paper cites Scalable global optimization via local Bayesian optimization.

Bayesian Optimization in Linear Time Scalable global optimization via local Bayesian optimization

Reference 5

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 00ec910e-d77c-4948-9ab5-f7407d458b7f · outbound

This paper cites CambridgeUniversity Press.

Bayesian Optimization in Linear Time CambridgeUniversity Press

Reference 6

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation edff470e-5bf4-45b5-ad8a-e6632fc602cf · outbound

This paper cites an unresolved cited work.

Bayesian Optimization in Linear Time Unresolved cited work

Reference 7

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 00fc9dce-4772-4181-b344-ae56815e9bc4 · outbound

This paper cites Scalable Bayesian opti- mization using Vecchia approximations of Gaussian processes.

Bayesian Optimization in Linear Time Scalable Bayesian opti- mization using Vecchia approximations of Gaussian processes

Reference 8

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 35eb9fd6-1504-4071-99cd-0a0eeb2688a0 · outbound

This paper cites Mopta 2008 benchmark.

Bayesian Optimization in Linear Time Mopta 2008 benchmark

Reference 9

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 235de5a4-06a1-4c64-a5ae-94ee911b714c · outbound

This paper cites Benchmark problems for constrained global optimizationwithhigh-dimensionalblack-boxfunctions.

Bayesian Optimization in Linear Time Benchmark problems for constrained global optimizationwithhigh-dimensionalblack-boxfunctions

Reference 10

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 936fd8a8-e78e-45e4-96e8-4ecc5dff0a1e · outbound

This paper cites Efficient global optimization of expensive black-box functions.

Bayesian Optimization in Linear Time Efficient global optimization of expensive black-box functions

Reference 11

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation a3cd62ca-9052-4c46-a585-803608572a69 · outbound

This paper cites Welch.EGO: Efficient Global Optimization.

Bayesian Optimization in Linear Time Welch.EGO: Efficient Global Optimization

Reference 12

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 8e471f34-cb15-4a21-a07e-77f5e81fd362 · outbound

This paper cites Rousseeuw.Finding Groups in Data: An Introduction to Cluster Analysis.

Bayesian Optimization in Linear Time Rousseeuw.Finding Groups in Data: An Introduction to Cluster Analysis

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-05-24T13:26:12.879960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 3ecf09dd-5188-43a7-b7d0-c21b4224a1da · outbound

This paper cites Navigating in High-Dimensional Search Space: A Hierarchical Bayesian Optimization Approach.

Bayesian Optimization in Linear Time Navigating in High-Dimensional Search Space: A Hierarchical Bayesian Optimization Approach

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-05-11T15:16:10.487785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 628bd2a7-0533-4692-be58-1bf0b17bff01 · outbound

This paper cites When Gaussian process meets big data: A review of scalable gps.IEEE transactions on neural networks and learning sys- tems.

Bayesian Optimization in Linear Time When Gaussian process meets big data: A review of scalable gps.IEEE transactions on neural networks and learning sys- tems

Reference 15

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 0745f150-761a-4313-9162-057bac7d2532 · outbound

This paper cites Choos- ing the sample size of a computer experiment: A practical guide.Technometrics.

Bayesian Optimization in Linear Time Choos- ing the sample size of a computer experiment: A practical guide.Technometrics

Reference 16

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation e2ecbf47-7cb6-43a7-90c6-2aa91b09b70c · outbound

This paper cites Geneticoptimiza- tion using derivatives: The rgenoud package for R.Journal of Statistical Software.

Bayesian Optimization in Linear Time Geneticoptimiza- tion using derivatives: The rgenoud package for R.Journal of Statistical Software

Reference 17

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation aea924e9-402b-44eb-9cd6-e11c2368962a · outbound

This paper cites Foundations of Machine Learning.

Bayesian Optimization in Linear Time Foundations of Machine Learning

Reference 18

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 7830345d-f589-4888-9879-385ba7814e82 · outbound

This paper cites Number153inAppliedMathematical Sciences.

Bayesian Optimization in Linear Time Number153inAppliedMathematical Sciences

Reference 19

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation afa9456a-5cca-42fe-b182-5dcd4d796fd9 · outbound

This paper cites DiceOptim: Kriging-Based Optimization for Computer Ex- periments.

Bayesian Optimization in Linear Time DiceOptim: Kriging-Based Optimization for Computer Ex- periments

Reference 20

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 57bbd130-916c-47d7-abb8-609ff69a7f03 · outbound

This paper cites R Foundation for Statistical Computing.

Bayesian Optimization in Linear Time R Foundation for Statistical Computing

Reference 21

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 8b9ad80b-bb43-4d40-97b0-8899a12a4e81 · outbound

This paper cites DiceK- riging, DiceOptim: Two R packages for the analysis of com- puter experiments by kriging-based metamodeling and opti- mization.Journal of Statistical Software.

Bayesian Optimization in Linear Time DiceK- riging, DiceOptim: Two R packages for the analysis of com- puter experiments by kriging-based metamodeling and opti- mization.Journal of Statistical Software

Reference 22

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation fae42cf0-14c5-4bc4-995c-5d47d8a421eb · outbound

This paper cites Virtual library of sim- ulation experiments: Test functions and datasets.

Bayesian Optimization in Linear Time Virtual library of sim- ulation experiments: Test functions and datasets

Reference 23

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation fdde65c2-61e1-4beb-9ee2-3b5d568e4bac · outbound

This paper cites an unresolved cited work.

Bayesian Optimization in Linear Time Unresolved cited work

Reference 24

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 8ee23cbe-5d07-4dd2-b96f-0ed4c79dab32 · outbound

This paper cites Blaschko.

Bayesian Optimization in Linear Time Blaschko

Reference 25

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation a2cbda49-da83-4225-b510-5ae3a0559587 · outbound

This paper cites Bayesian op- timization is superior to random search for machine learning hyperparameter tuning: Analysis of the black-box optimiza- tion challenge 2020.

Bayesian Optimization in Linear Time Bayesian op- timization is superior to random search for machine learning hyperparameter tuning: Analysis of the black-box optimiza- tion challenge 2020

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-05-24T13:26:12.897813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 1e4c709b-81f4-4654-9242-64b9a8e40406 · outbound

This paper cites Learning search space partition for black-box optimization using Monte Carlo tree search.Advances in Neural Information Processing Systems.

Bayesian Optimization in Linear Time Learning search space partition for black-box optimization using Monte Carlo tree search.Advances in Neural Information Processing Systems

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-05-24T13:26:12.844762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation dac8c411-681a-46d6-9a47-81826f44693d · outbound

This paper cites Bayesianoptimizationinabilliondimen- sionsviarandomembeddings.JournalofArtificialIntelligence Research.

Bayesian Optimization in Linear Time Bayesianoptimizationinabilliondimen- sionsviarandomembeddings.JournalofArtificialIntelligence Research

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-05-24T13:26:12.878978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 52615995-51da-49e9-9459-f59200da50e3 · outbound

This paper cites ScalableBayesianoptimizationviafocalizedsparse Gaussian processes.

Bayesian Optimization in Linear Time ScalableBayesianoptimizationviafocalizedsparse Gaussian processes

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-05-24T13:26:12.909133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 57ab6e0d-56ad-4920-9527-7aca3f393c13 · outbound

This paper cites Using trajec- tory data to improve Bayesian optimization for reinforcement learning.Journal of Machine Learning Research.

Bayesian Optimization in Linear Time Using trajec- tory data to improve Bayesian optimization for reinforcement learning.Journal of Machine Learning Research

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-05-24T13:26:12.889208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 11a64ca0-ed91-408c-9e13-e8cef2411cb4 · outbound

This paper cites Are ran- domdecompositionsallweneedinhighdimensionalBayesian optimisation? InInternational Conference on Machine Learn- ing.

Bayesian Optimization in Linear Time Are ran- domdecompositionsallweneedinhighdimensionalBayesian optimisation? InInternational Conference on Machine Learn- ing

Reference 31

Resolution
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raw_fallback, observed 2026-05-24T13:26:12.884093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Pith citing papers

No inbound Pith citation observations are available.