Fumagalli, F., Muschalik, M., Hüllermeier, E., &; Hammer, B. (2023). Incremental permutation feature importance (iPFI): towards online explanations on data streams. Machine Learning.https://doi.org/10.1007/s10994-023-06385-y 
LibreCat: /record/48777
DOI: 10.1007/s10994-023-06385-y

Fumagalli, F., Muschalik, M., Hüllermeier, E., &; Hammer, B. (2023). Incremental permutation feature importance (iPFI): towards online explanations on data streams. Machine Learning, 112(12), 4863–4903.https://doi.org/10.1007/s10994-023-06385-y 
LibreCat: /record/50262
DOI: 10.1007/s10994-023-06385-y

Haddenhorst, B., Bengs, V., &; Hüllermeier, E. (2021). On testing transitivity in online preference learning. Machine Learning, 2063–2084.https://doi.org/10.1007/s10994-021-06026-2 
LibreCat: /record/25035
DOI: 10.1007/s10994-021-06026-2

Mohr, F., Wever, M. D., &; Hüllermeier, E. (2018). ML-Plan: Automated Machine Learning via Hierarchical Planning. Machine Learning, 1495–1515.https://doi.org/10.1007/s10994-018-5735-z 
LibreCat: /record/3510
DOI: 10.1007/s10994-018-5735-z