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Paper Citation Record · LEDGER

Privacy-Aware Collaborative and Distributed Bayesian Optimization

As of 17 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2607.11600.

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

pith.paper-citation-record.v1
2607.11600 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-14T04:29:35.143178Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

20 of 20 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3bf1ad42-a613-4672-8151-f988e5859ee9 · outbound

This paper cites Exploring machine learning for semiconductor process optimization: A systematic review.IEEE Transactions on Artificial Intelligence, 5(12):5969–5989, 2024.

Privacy-Aware Collaborative and Distributed Bayesian Optimization Exploring machine learning for semiconductor process optimization: A systematic review.IEEE Transactions on Artificial Intelligence, 5(12):5969–5989, 2024

Reference 1

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Source-reported events for the cited work

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source=pdf_text observed=2026-07-14T04:29:35.143178Z digest=sha256:9545ff8e92792e163b107220f4b55c276f719991cfd9b29d6a0e54ce89628dfd

Observation 6b47fff1-d8d9-4fc1-8122-1a5e216ac16c · outbound

This paper cites Machine-learning-assisted materials discovery using failed experiments.Nature, 533(7601):73–76, 2016.

Privacy-Aware Collaborative and Distributed Bayesian Optimization Machine-learning-assisted materials discovery using failed experiments.Nature, 533(7601):73–76, 2016

Reference 2

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Source-reported events for the cited work

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source=pdf_text observed=2026-07-14T04:29:35.143178Z digest=sha256:f1882bc6ea7d0488f86cd8548614013cb219ba1e6a242c44e80a1ab6ba5e0e65

Observation d0f97970-b116-4560-9eda-6314cd4967ee · outbound

This paper cites From materials to management: The expanding role of design of experiments in advanced battery technologies.Energy Storage Materials, page 104890, 2026.

Privacy-Aware Collaborative and Distributed Bayesian Optimization From materials to management: The expanding role of design of experiments in advanced battery technologies.Energy Storage Materials, page 104890, 2026

Reference 3

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source=pdf_text observed=2026-07-14T04:29:35.143178Z digest=sha256:ca45d7dc44e0886df8fbe0a22180ced8fb1839911516dc5c7fbc8355a02d810c

Observation 683ceed7-a613-4e5b-bfcb-8c9d6dadc10e · outbound

This paper cites Federated machine learning: Concept and applications.ACM Transactions on Intelligent Systems and Technology (TIST), 10(2):1–19, 2019.

Privacy-Aware Collaborative and Distributed Bayesian Optimization Federated machine learning: Concept and applications.ACM Transactions on Intelligent Systems and Technology (TIST), 10(2):1–19, 2019

Reference 4

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Source-reported events for the cited work

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source=pdf_text observed=2026-07-14T04:29:35.143178Z digest=sha256:85bbe8c5541ca643bdbe8e4606d3e792d0d543df52cc15f8dabe5deb73e61e4f

Observation 681afe3d-33da-433f-b6f4-ec2a6a147cfa · outbound

This paper cites A Tutorial on Bayesian Optimization.

Privacy-Aware Collaborative and Distributed Bayesian Optimization A Tutorial on Bayesian Optimization

Reference 5

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source=pdf_text observed=2026-07-14T04:29:35.143178Z digest=sha256:ab1955a643816ab40407b5ae8a90f4f73f3a493102d320111c40a9f27e0e6160

Observation 498cffe8-3c63-47ee-bb89-78724f24cc05 · outbound

This paper cites Scalable pac-bayesian meta-learning via the pac-optimal hyper-posterior: From theory to practice.Journal of Machine Learning Research, 24(386):1–62, 2023.

Privacy-Aware Collaborative and Distributed Bayesian Optimization Scalable pac-bayesian meta-learning via the pac-optimal hyper-posterior: From theory to practice.Journal of Machine Learning Research, 24(386):1–62, 2023

Reference 6

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source=pdf_text observed=2026-07-14T04:29:35.143178Z digest=sha256:bdbef9950672b599d545caf77620388fd3dab4ad9e231e626aefa7a9df22f13d

Observation e9bd84a0-4c93-49fa-a8ed-3e888d66baca · outbound

This paper cites Deep leakage from gradients.

Privacy-Aware Collaborative and Distributed Bayesian Optimization Deep leakage from gradients

Reference 7

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source=pdf_text observed=2026-07-14T04:29:35.143178Z digest=sha256:ba6ef221be1974829b500dee4d9eed9bb6b34c9241ff3a6f9942738abd6861e5

Observation c1eee994-8505-4e01-b7fb-5b136a326ae7 · outbound

This paper cites Taking the human out of the loop: A review of bayesian optimization.Proceedings of the IEEE, 104(1):148–175, 2015.

Privacy-Aware Collaborative and Distributed Bayesian Optimization Taking the human out of the loop: A review of bayesian optimization.Proceedings of the IEEE, 104(1):148–175, 2015

Reference 8

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source=pdf_text observed=2026-07-14T04:29:35.143178Z digest=sha256:652be006e3bdf40272f82d33577b67240480a70c449d699985edb29802bbd0e1

Observation b75de86f-9edc-4618-a63d-123d0a2ff164 · outbound

This paper cites Meta-learning reliable priors in the function space.Advances in Neural Information Processing Systems, 34:280–293, 2021.

Privacy-Aware Collaborative and Distributed Bayesian Optimization Meta-learning reliable priors in the function space.Advances in Neural Information Processing Systems, 34:280–293, 2021

Reference 9

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source=pdf_text observed=2026-07-14T04:29:35.143178Z digest=sha256:2c1b89410bd6f585fcd7b3c076734473bcf6f043b7190d35c626cade5e3852e4

Observation 5def7aac-f562-4900-8ba6-222f0960bf79 · outbound

This paper cites Communication-efficient learning of deep networks from decentralized data.

Privacy-Aware Collaborative and Distributed Bayesian Optimization Communication-efficient learning of deep networks from decentralized data

Reference 10

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source=pdf_text observed=2026-07-14T04:29:35.143178Z digest=sha256:7eb1a3e9cdb3740df47186cbee58c087936c025fe8bd011db547753e36a481ec

Observation 1d5b7819-8254-4add-a95e-ae8827bf62ca · outbound

This paper cites Collaborative and distributed bayesian optimization via consensus.IEEE Transactions on Automation Science and Engineering, 22:11343–11355, 2025.

Privacy-Aware Collaborative and Distributed Bayesian Optimization Collaborative and distributed bayesian optimization via consensus.IEEE Transactions on Automation Science and Engineering, 22:11343–11355, 2025

Reference 11

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source=pdf_text observed=2026-07-14T04:29:35.143178Z digest=sha256:5f7df9a37dc6555f293fb1cde36b2d96c96ec814a628d7d349526b8c4ef7adbd

Observation e448222f-8f71-43b3-ba27-53f37de536c0 · outbound

This paper cites Collaborative Contextual Bayesian Optimization.

Privacy-Aware Collaborative and Distributed Bayesian Optimization Collaborative Contextual Bayesian Optimization

Reference 12

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Source-reported events for the cited work

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source=pdf_text observed=2026-07-14T04:29:35.143178Z digest=sha256:e640dbd0873628342d542126985c560ce0298ea960da569c78790df14288a481

Observation e9490063-0e8d-46c3-8858-f04457be4c30 · outbound

This paper cites Collaborative bayesian optimization via wasserstein barycenters.

Privacy-Aware Collaborative and Distributed Bayesian Optimization Collaborative bayesian optimization via wasserstein barycenters

Reference 13

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source=pdf_text observed=2026-07-14T04:29:35.143178Z digest=sha256:109b5435263752902b1fb2dc8f2a2e8ef7c4e08d2e0c0d3c90fb4ceb96be6a76

Observation 4500d8b7-9929-4b99-8b5a-5b886a1952ee · outbound

This paper cites Differential privacy: Gradient leakage attacks in federated learning environments.arXiv preprint arXiv:2510.23931, 2025.

Privacy-Aware Collaborative and Distributed Bayesian Optimization Differential privacy: Gradient leakage attacks in federated learning environments.arXiv preprint arXiv:2510.23931, 2025

Reference 14

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source=pdf_text observed=2026-07-14T04:29:35.143178Z digest=sha256:b625ee020857498d4445fbb5b72da9a17e3dc5b0fe18c1a325e81436dc2daa74

Observation 89609fa3-cf38-456d-aae8-19104ff15580 · outbound

This paper cites Deep learning with differential privacy.

Privacy-Aware Collaborative and Distributed Bayesian Optimization Deep learning with differential privacy

Reference 15

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source=pdf_text observed=2026-07-14T04:29:35.143178Z digest=sha256:6e1132cf72ea31d79b58ce7ac6f75dd53b91c926fb585ff4f105ee1ee2710d24

Observation 18bfa907-04ad-4bbb-8f6e-be24d8c99eea · outbound

This paper cites Differentially Private Meta-Learning.

Privacy-Aware Collaborative and Distributed Bayesian Optimization Differentially Private Meta-Learning

Reference 16

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source=pdf_text observed=2026-07-14T04:29:35.143178Z digest=sha256:894093535def5fc054e8a606e8c3d275786b3e703c647dd8ebd171d051182307

Observation 28f1f80c-43db-40ec-8ef8-98a3de0c70ea · outbound

This paper cites Gaussian processes in machine learning.

Privacy-Aware Collaborative and Distributed Bayesian Optimization Gaussian processes in machine learning

Reference 17

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source=pdf_text observed=2026-07-14T04:29:35.143178Z digest=sha256:5c5880a44edbd6a9fbc54588acc05faecc2eb01a538a887936d1a8000165c0f4

Observation bd86581e-3b5a-48b3-9adf-842200295a42 · outbound

This paper cites mpi4py.futures: Mpi-based asynchronous task execution for python.IEEE Transactions on Parallel and Distributed Systems, 34(2):611–622, 2023.

Privacy-Aware Collaborative and Distributed Bayesian Optimization mpi4py.futures: Mpi-based asynchronous task execution for python.IEEE Transactions on Parallel and Distributed Systems, 34(2):611–622, 2023

Reference 18

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source=pdf_text observed=2026-07-14T04:29:35.143178Z digest=sha256:e1262807dab2d31270ed449a895e68e43386b5ccb8feabcd0ca7f582e3f97b64

Observation 6550ca85-678f-45b1-a695-65d6f5c1066c · outbound

This paper cites Virtual library of simulation experiments: Test functions and datasets.

Privacy-Aware Collaborative and Distributed Bayesian Optimization Virtual library of simulation experiments: Test functions and datasets

Reference 19

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source=pdf_text observed=2026-07-14T04:29:35.143178Z digest=sha256:9d95ec1ad21f5fa442cf68de8b4f84d13c680cab40c94523277b3deb65ec7012

Observation 2f801830-1ab4-4ab6-bc78-b01f2398a8f2 · outbound

This paper cites Are we forgetting about compositional optimisers in bayesian optimisation?Journal of Machine Learning Research, 22(160):1–78, 2021.

Privacy-Aware Collaborative and Distributed Bayesian Optimization Are we forgetting about compositional optimisers in bayesian optimisation?Journal of Machine Learning Research, 22(160):1–78, 2021

Reference 20

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source=pdf_text observed=2026-07-14T04:29:35.143178Z digest=sha256:35022c62d0d7d0a3de73c8230e58b167c670f36eb8bcff61d1465f50c435b39a

Pith citing papers

No inbound Pith citation observations are available.