Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T04:40:28.262423Z
Paper Citation Record · LEDGER
As of 9 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2608.06351.
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 2026-08-07T04:40:28.262423Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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
31 of 31 outbound references displayed
External citation measurements
No source-named external measurement is stored.
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Challenges in Evaluating Explanation Methods for Static and Evolving Data Nature Machine Intelligence5, 1006–1019 (09 2023)
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Challenges in Evaluating Explanation Methods for Static and Evolving Data Information fusion58, 82–115 (2020)
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Challenges in Evaluating Explanation Methods for Static and Evolving Data Advances in Neural Information Processing Systems29(2016) 12 Jerzy Stefanowski
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Challenges in Evaluating Explanation Methods for Static and Evolving Data In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
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Challenges in Evaluating Explanation Methods for Static and Evolving Data Towards A Rigorous Science of Interpretable Machine Learning
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Challenges in Evaluating Explanation Methods for Static and Evolving Data Master’s thesis, Poznan University of Technology, Faculty of Computer Science and Telecommunication (2023)
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Challenges in Evaluating Explanation Methods for Static and Evolving Data Unifying Perspectives: Plausible Counterfactual Explanations on Global, Group-wise, and Local Levels
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Challenges in Evaluating Explanation Methods for Static and Evolving Data Artificial Intelligence291, 103428 (02 2021)
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Challenges in Evaluating Explanation Methods for Static and Evolving Data Data Mining and Knowledge Discovery pp
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Challenges in Evaluating Explanation Methods for Static and Evolving Data ACM computing surveys (CSUR)51(5), 1–42 (2018)
Reference 10
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Challenges in Evaluating Explanation Methods for Static and Evolving Data Statistical Science 21(1), 1–14 (2006)
Reference 11
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Challenges in Evaluating Explanation Methods for Static and Evolving Data Neurocomputing555, 126640 (2023)
Reference 12
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Challenges in Evaluating Explanation Methods for Static and Evolving Data ACM Computing Surveys55(2), 1–38 (2022)
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Challenges in Evaluating Explanation Methods for Static and Evolving Data In: Pro- ceedings of the Fortieth AAAI Conference on Artificial Intelligence
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Challenges in Evaluating Explanation Methods for Static and Evolving Data Applied Sciences12(19) (2022)
Reference 16
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Challenges in Evaluating Explanation Methods for Static and Evolving Data IEEE Transactions on Knowledge and Data EngineeringPP, 1–1 (10 2018)
Reference 17
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Challenges in Evaluating Explanation Methods for Static and Evolving Data ACM Computing Surveys54(6) (Jul 2021)
Reference 18
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Challenges in Evaluating Explanation Methods for Static and Evolving Data Frontiers in Artificial Intelligence (2022)
Reference 19
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Challenges in Evaluating Explanation Methods for Static and Evolving Data In: Proceedings of the 43rd International Conference on Machine Learning (ICML) (2026)
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Reference 21
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Challenges in Evaluating Explanation Methods for Static and Evolving Data Computers and Electrical Engineering118, 109370 (2024)
Reference 23
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Challenges in Evaluating Explanation Methods for Static and Evolving Data In: Asian Conference on Intelligent Information and Database Systems ACIIDS 2023
Reference 24
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Challenges in Evaluating Explanation Methods for Static and Evolving Data International Journal of Applied Mathematics and Computer Science34(1), 119– 133 (2024)
Reference 25
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Reference 26
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Challenges in Evaluating Explanation Methods for Static and Evolving Data In: Joint European Confer- ence on Machine Learning and Knowledge Discovery in Databases
Reference 27
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Challenges in Evaluating Explanation Methods for Static and Evolving Data Investigating the Relationship Between Debiasing and Artifact Removal using Saliency Maps
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Challenges in Evaluating Explanation Methods for Static and Evolving Data IEEE AccessPP, 1–1 (01 2025)
Reference 29
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Challenges in Evaluating Explanation Methods for Static and Evolving Data ACM Computing Surveys (2020)
Reference 30
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Challenges in Evaluating Explanation Methods for Static and Evolving Data In: Proceedins of ECAI 2024 Conference, pp
Reference 31
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No inbound Pith citation observations are available.