PUB - Publikationen an der Universität Bielefeld

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• [8]
  

  2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2991394

  SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions through Stratification
  Kolpaczki P, Muschalik M, Fumagalli F, Hammer B, Hüllermeier E (2024)
  In: International Conference on Artificial Intelligence and Statistics, 2-4 May 2024, Palau de Congressos, Valencia, Spain. Dasgupta S, Mandt S, Li Y (Eds); Proceedings of Machine Learning Research, 238. San Diego: PMLR: 3520-3528.

  PUB | WoS | arXiv
   
• [7]
  

  2024 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2988165

  Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles
  Muschalik M, Fumagalli F, Hammer B, Hüllermeier E (2024)
  Proceedings of the AAAI Conference on Artificial Intelligence 38(13): 14388-14396.

  PUB | DOI
   
• [6]
  

  2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983942

  iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios
  Muschalik M, Fumagalli F, Jagtani R, Hammer B, Hüllermeier E (2023)
  In: Explainable Artificial Intelligence. First World Conference, xAI 2023, Lisbon, Portugal, July 26–28, 2023, Proceedings, Part I. Longo L (Ed); Communications in Computer and Information Science. Cham: Springer Nature Switzerland: 177-194.

  PUB | DOI | WoS
   
• [5]
  

  2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2987580

  SHAP-IQ: Unified Approximation of any-order Shapley Interactions
  Fumagalli F, Muschalik M, Kolpaczki P, Hüllermeier E, Hammer B (2023)
  In: Advances in Neural Information Processing Systems 36 (NeurIPS 2023). Advances in Neural Information Processing Systems. .

  PUB | Download (ext.) | WoS | arXiv
   
• [4]
  

  2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2983756

  On Feature Removal for Explainability in Dynamic Environments
  Fumagalli F, Muschalik M, Hüllermeier E, Hammer B (2023)
  In: ESANN 2023 proceedings. 83-88.

  PUB | DOI
   
• [3]
  

  2023 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2983727

  Incremental permutation feature importance (iPFI): towards online explanations on data streams
  Fumagalli F, Muschalik M, Hüllermeier E, Hammer B (2023)
  Machine Learning .

  PUB | DOI | WoS
   
• [2]
  

  2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983943

  iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams
  Muschalik M, Fumagalli F, Hammer B, Hüllermeier E (2023)
  In: Machine Learning and Knowledge Discovery in Databases: Research Track. European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part III. Koutra D, Plant C, Gomez Rodriguez M, Baralis E, Bonchi F (Eds); Lecture Notes in Computer Science. Cham: Springer Nature Switzerland: 428-445.

  PUB | DOI
   
• [1]
  

  2022 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2964421

  Agnostic Explanation of Model Change based on Feature Importance
  Muschalik M, Fumagalli F, Hammer B, Hüllermeier E (2022)
  KI - Künstliche Intelligenz.

  PUB | DOI | Download (ext.) | WoS