PUB - Publikationen an der Universität Bielefeld

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• [7]
  

  2024 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2988165

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

  PUB | DOI
   
• [6]
  

  2023 | Konferenzbeitrag | PUB-ID: 2987580

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

  PUB | Download (ext.) | arXiv
   
• [5]
  

  2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2983756

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

  PUB | DOI
   
• [4]
  

  2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983943

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

  PUB | DOI
   
• [3]
  

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

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

  PUB | DOI | WoS
   
• [2]
  

  2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983942

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

  PUB | DOI
   
• [1]
  

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

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

  PUB | DOI | Download (ext.) | WoS