Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T11:54:26.905697Z
Paper Citation Record · LEDGER
As of 8 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 0 inbound Pith citation observations for arXiv:2506.01230.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T11:54:26.905697Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
62 of 62 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation e5c411fd-f829-4795-87ae-bfd77cdcfb36 · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Regulation (EU) 2024/1689 of the European Parliament and of the Council on Artificial Intelligence
Reference 1
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Observation 289fcb5a-68cf-4d68-98db-2396acc6d96c · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Codebase for SAVAGE
Reference 2
Source-reported events for the cited work
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Observation 375bf85c-60aa-4101-84e4-d755a8aafc37 · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption REIN: A Comprehensive Benchmark Framework for Data Cleaning Methods in ML Pipelines
Reference 3
Source-reported events for the cited work
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Observation a75c299c-6891-44fa-a638-7685f1705a28 · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Unresolved cited work
Reference 4
Source-reported events for the cited work
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Observation c9835f2e-ddf2-4894-9435-b214b3da8e9c · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Poisoning Attacks against Support Vector Machines
Reference 5
Source-reported events for the cited work
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Observation 6b6bd40b-762b-4cc5-a8b2-4772ec229415 · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption The Effects of Data Quality on Machine Learning Performance on Tabular Data
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Observation 5a4608b7-2e91-4bbb-888b-6ddd477da902 · outbound
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Observation 8e439b08-07ae-4794-bd05-c98393280e0a · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption 2016.Practical machine learning with H2O: powerful, scalable techniques for deep learning and AI
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Stress-Testing ML Pipelines with Adversarial Data Corruption Unresolved cited work
Reference 9
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Observation b4b8d264-78f8-4143-a744-e1105e7c31ae · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Unresolved cited work
Reference 10
Source-reported events for the cited work
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Observation 96d56041-2d33-407b-a619-15c7a5e6ca36 · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Unresolved cited work
Reference 11
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Observation 3be4e40d-a078-48f5-8b77-e7cf236e015b · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning
Reference 12
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Observation 2a8a111e-c6ac-4cca-bb0a-ad9328b37b71 · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Unresolved cited work
Reference 13
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Observation 4af52504-d53c-4323-9802-e7a01224bf4e · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Unresolved cited work
Reference 14
Source-reported events for the cited work
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Observation 4aee2b02-ebc8-48e1-aa10-4a8d13677b6e · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption 2012.Missing data: Analysis and design
Reference 15
Source-reported events for the cited work
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Observation 469d4eef-7875-4575-823e-a573763ca07f · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Unresolved cited work
Reference 16
Source-reported events for the cited work
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Observation 5312fe1f-b66f-4822-afe0-6982eead7a39 · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Unresolved cited work
Reference 17
Source-reported events for the cited work
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Observation a82e45a5-19cc-46e5-bea7-2a0dfccd07c4 · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Investigating Labeler Bias in Face Annotation for Machine Learning
Reference 18
Source-reported events for the cited work
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Observation 39448dcd-1f1b-42ad-858d-111ee1ea4628 · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Unresolved cited work
Reference 19
Source-reported events for the cited work
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Observation 9b3492b7-f21f-4d70-a9d1-3c35ed76e2c7 · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Unresolved cited work
Reference 20
Source-reported events for the cited work
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Observation 01876e31-e8c6-4dbf-ad48-a7e9fe71da6d · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Unresolved cited work
Reference 21
Source-reported events for the cited work
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Observation 35a3d42b-1796-47e7-a5ae-cad350bfc579 · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Unresolved cited work
Reference 22
Source-reported events for the cited work
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Observation be7b83f5-df58-4724-9c85-2b256fd0337a · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Unresolved cited work
Reference 23
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Observation ccf76ebf-a75e-4f99-8da8-657f372d11e0 · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Unresolved cited work
Reference 24
Source-reported events for the cited work
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Observation a991c2ab-e9bc-40a8-8e86-092159b8576f · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption BoostClean: Automated Error Detection and Repair for Machine Learning
Reference 25
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Observation b4f3595d-033f-48c7-a501-b00bbb6a3eb3 · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Unresolved cited work
Reference 26
Source-reported events for the cited work
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Observation c227212b-1754-45ef-a0e4-f091d330168c · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Unresolved cited work
Reference 27
Source-reported events for the cited work
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Observation 48b5821f-0355-4a12-9c5b-d8a9efe62765 · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Unresolved cited work
Reference 28
Source-reported events for the cited work
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Observation 618aae0f-be31-4f01-b735-ba1b38267783 · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Unresolved cited work
Reference 29
Source-reported events for the cited work
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Observation 802bfda8-c9ad-4523-8e8d-ed42977495a5 · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Unresolved cited work
Reference 30
Source-reported events for the cited work
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Observation e13f4f7b-a2c4-4b1b-9d8c-638dcafa24b5 · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Unresolved cited work
Reference 31
Source-reported events for the cited work
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Observation b3cffc80-bd46-4ed8-b01f-a1d76c3efa0b · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Indiscriminate Data Poisoning Attacks on Neural Networks
Reference 32
Source-reported events for the cited work
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Observation ed3ec94b-3471-4d52-bbda-a59a77cf0beb · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Unresolved cited work
Reference 33
Source-reported events for the cited work
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Observation 34e83cd5-0512-4609-a503-6ee3c4f6edf4 · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Unresolved cited work
Reference 34
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Observation 109aa4d3-d6ce-41e0-9b66-fe3911154cb9 · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Unresolved cited work
Reference 35
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Observation c7e807a7-43ac-4c88-b534-c2e07360514c · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption 2024.Artificial Intelligence Risk Management Framework (1.0) – Generative AI Profile
Reference 36
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Observation acb152da-3e8b-43de-af7f-d87774a9646a · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption 2009.Causality
Reference 37
Source-reported events for the cited work
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Observation d1d6b7a9-7f15-4128-aa4f-e75347d8fe6b · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Pedregosa, G
Reference 38
Source-reported events for the cited work
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Observation bb66c0c1-47d5-4f34-93a6-7ecb9a7e110f · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Unresolved cited work
Reference 39
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Observation 1320b36a-645c-4231-b64b-51033c5f232d · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Unresolved cited work
Reference 40
Source-reported events for the cited work
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Observation 23b1d840-25d7-41c6-9557-44fc2e70580c · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Unresolved cited work
Reference 41
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Observation 2b50507f-6b2c-45b0-99e0-3b93ba3dd0f5 · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Unresolved cited work
Reference 42
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Observation 0eef91ae-40f3-4ac1-bfbd-21df52516244 · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Unresolved cited work
Reference 43
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Observation 2685127d-3f9c-4039-b1ae-6d3f1af60241 · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Unresolved cited work
Reference 44
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Observation cf762c32-cfd5-4ad5-b17c-f5c20a74b3b7 · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Unresolved cited work
Reference 45
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Observation a64b94dd-39a3-4b0e-8a97-6069245ab1a1 · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Unresolved cited work
Reference 46
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Observation 8f10eb71-12e3-439d-8813-6e49073a4f15 · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Unresolved cited work
Reference 47
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Observation 7f3ae5b3-bd4d-4f24-b07d-7b14476e6c19 · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Unresolved cited work
Reference 48
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Observation f7f610b5-00ef-4c16-a333-84c4fafef418 · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Unresolved cited work
Reference 49
Source-reported events for the cited work
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Observation efa966b4-a9fc-4428-9389-278c86a29987 · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Unresolved cited work
Reference 50
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Observation e0629252-0aca-49d4-8f86-e289d3c1e870 · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Unresolved cited work
Reference 51
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Observation ec50611d-fad3-4cf1-8797-da537cbb9f8b · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Unresolved cited work
Reference 52
Source-reported events for the cited work
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Observation c4e73516-7489-4295-bbc3-02ce4bca83ee · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Zemel, Yu Wu, Kevin Swersky, Toniann Pitassi, and Cynthia Dwork
Reference 53
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Observation f181a355-7ea0-4e3b-aff9-8ee2ad1a0cbd · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Unresolved cited work
Reference 54
Source-reported events for the cited work
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Observation d7aa7107-8553-4536-bcdc-42c2f23a5fcf · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Unresolved cited work
Reference 55
Source-reported events for the cited work
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Observation b440acd8-a1d4-4349-ae0c-a3e56209d1dd · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption InICML (3) (JMLR Workshop and Conference Proceedings), Vol
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Observation 4ca936c6-dfac-4853-82c8-1ca36b97dd70 · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption unknown
Reference 2017
Source-reported events for the cited work
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Observation 5f9cd1b7-239b-48db-82ae-e3ae84574ec9 · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Unresolved cited work
Reference 2018
Source-reported events for the cited work
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Observation 8c63412e-f0e9-4aad-9fdb-8eab27dd23de · outbound
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Observation f146a1e5-510a-489d-b325-ce2f12fd02c1 · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption InNeurIPS ML Safety Workshop
Reference 2022
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Observation e8405f72-c5b4-4196-87e2-d6512dc04b0b · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Unresolved cited work
Reference 2023
Source-reported events for the cited work
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Observation 2eb47ec1-52a4-415d-a94f-f794c9a6cea7 · outbound
Stress-Testing ML Pipelines with Adversarial Data Corruption Still More Shades of Null: An Evaluation Suite for Responsible Missing Value Imputation
Reference 2024
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
No inbound Pith citation observations are available.