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439 Publications
2024 | Journal Article | LibreCat-ID: 53073
M. Muschalik, F. Fumagalli, B. Hammer, and E. Huellermeier, “Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 13, pp. 14388–14396, 2024, doi: 10.1609/aaai.v38i13.29352.
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| DOI
2023 | Preprint | LibreCat-ID: 44512 | 
S. Uhlemeyer, J. Lienen, E. Hüllermeier, and H. Gottschalk, “Detecting Novelties with Empty Classes,” arXiv:2305.00983. 2023.
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| arXiv
2023 | Conference Paper | LibreCat-ID: 31880 |
D. A. Nguyen, R. Levie, J. Lienen, G. Kutyniok, and E. Hüllermeier, “Memorization-Dilation: Modeling Neural Collapse Under Noise,” presented at the International Conference on Learning Representations, ICLR, Kigali, Ruanda, 2023.
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2023 | Book Chapter | LibreCat-ID: 45884 |
J. M. Hanselle et al., “Configuration and Evaluation,” in On-The-Fly Computing -- Individualized IT-services in dynamic markets, vol. 412, C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, and H. Wehrheim, Eds. Paderborn: Heinz Nixdorf Institut, Universität Paderborn, 2023, pp. 85–104.
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| DOI
2023 | Book Chapter | LibreCat-ID: 45886 |
H. Wehrheim, E. Hüllermeier, S. Becker, M. Becker, C. Richter, and A. Sharma, “Composition Analysis in Unknown Contexts,” in On-The-Fly Computing -- Individualized IT-services in dynamic markets, vol. 412, C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, and H. Wehrheim, Eds. Paderborn: Heinz Nixdorf Institut, Universität Paderborn, 2023, pp. 105–123.
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2023 | Preprint | LibreCat-ID: 45911 |
J. Lienen and E. Hüllermeier, “Mitigating Label Noise through Data Ambiguation,” arXiv:2305.13764. 2023.
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| arXiv
2023 | Journal Article | LibreCat-ID: 21600
M. Dellnitz et al., “Efficient time stepping for numerical integration using reinforcement learning,” SIAM Journal on Scientific Computing, vol. 45, no. 2, pp. A579–A595, 2023, doi: 10.1137/21M1412682.
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| arXiv
2023 | Conference Paper | LibreCat-ID: 51373
J. M. Hanselle, J. Fürnkranz, and E. Hüllermeier, “Probabilistic Scoring Lists for Interpretable Machine Learning,” in 26th International Conference on Discovery Science , Porto, 2023, vol. 14050, pp. 189–203, doi: 10.1007/978-3-031-45275-8_13.
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2023 | Book Chapter | LibreCat-ID: 48776
M. Muschalik, F. Fumagalli, B. Hammer, and E. Huellermeier, “iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams,” in Machine Learning and Knowledge Discovery in Databases: Research Track, Cham: Springer Nature Switzerland, 2023.
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2023 | Conference Paper | LibreCat-ID: 48775
F. Fumagalli, M. Muschalik, E. Hüllermeier, and B. Hammer, “On Feature Removal for Explainability in Dynamic Environments,” presented at the ESANN 2023 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges (Belgium) and online, 2023, doi: 10.14428/esann/2023.es2023-148.
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| DOI
2023 | Conference Paper | LibreCat-ID: 52230
F. Fumagalli, M. Muschalik, P. Kolpaczki, E. Hüllermeier, and B. Hammer, “SHAP-IQ: Unified Approximation of any-order Shapley Interactions,” in NeurIPS 2023 - Advances in Neural Information Processing Systems, 2023, vol. 36, pp. 11515--11551.
LibreCat
2022 | Conference Paper | LibreCat-ID: 32311
A. Sharma, V. Melnikov, E. Hüllermeier, and H. Wehrheim, “Property-Driven Testing of Black-Box Functions,” in Proceedings of the 10th IEEE/ACM International Conference on Formal Methods in Software Engineering (FormaliSE), 2022, pp. 113–123.
LibreCat
2022 | Conference Paper | LibreCat-ID: 34542
A. Campagner, J. Lienen, E. Hüllermeier, and D. Ciucci, “Scikit-Weak: A Python Library for Weakly Supervised Machine Learning,” in Lecture Notes in Computer Science, Suzhou, China, 2022, vol. 13633, pp. 57–70.
LibreCat
2022 | Preprint | LibreCat-ID: 31546 |
J. Lienen, C. Demir, and E. Hüllermeier, “Conformal Credal Self-Supervised Learning,” arXiv:2205.15239. 2022.
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2022 | Journal Article | LibreCat-ID: 33090
K. Gevers, A. Tornede, M. D. Wever, V. Schöppner, and E. Hüllermeier, “A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials,” Welding in the World, 2022, doi: 10.1007/s40194-022-01339-9.
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