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452 Publications
2023 | Conference Paper | LibreCat-ID: 51209
J. M. Hanselle, J. Kornowicz, S. Heid, K. Thommes, and E. Hüllermeier, “Comparing Humans and Algorithms in Feature Ranking: A Case-Study in the Medical Domain,” in LWDA’23: Learning, Knowledge, Data, Analysis. , 2023.
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2023 | Conference Paper | LibreCat-ID: 48778
M. Muschalik, F. Fumagalli, R. Jagtani, B. Hammer, and E. Huellermeier, “iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios,” 2023, doi: 10.1007/978-3-031-44064-9_11.
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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 - European Conference (ECML PKDD), 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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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 Advances in Neural Information Processing Systems (NeurIPS), 2023, vol. 36, pp. 11515--11551.
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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.
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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.
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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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2022 | Journal Article | LibreCat-ID: 48780
M. Muschalik, F. Fumagalli, B. Hammer, and E. Huellermeier, “Agnostic Explanation of Model Change based on Feature Importance,” KI - Künstliche Intelligenz, vol. 36, no. 3–4, pp. 211–224, 2022, doi: 10.1007/s13218-022-00766-6.
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2021 | Journal Article | LibreCat-ID: 24143
J. P. Drees et al., “Automated Detection of Side Channels in Cryptographic Protocols: DROWN the ROBOTs!,” 14th ACM Workshop on Artificial Intelligence and Security, 2021.
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2021 | Journal Article | LibreCat-ID: 24148
A. Ramaswamy and E. Hüllermeier, “Deep Q-Learning: Theoretical Insights from an Asymptotic Analysis,” IEEE Transactions on Artificial Intelligence (to appear), 2021.
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2021 | Journal Article | LibreCat-ID: 21004
M. D. Wever, A. Tornede, F. Mohr, and E. Hüllermeier, “AutoML for Multi-Label Classification: Overview and Empirical Evaluation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1–1, 2021, doi: 10.1109/tpami.2021.3051276.
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2021 | Journal Article | LibreCat-ID: 21092
F. Mohr, M. D. Wever, A. Tornede, and E. Hüllermeier, “Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence.
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2021 | Conference Paper | LibreCat-ID: 21570
T. Tornede, A. Tornede, M. D. Wever, and E. Hüllermeier, “Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance,” presented at the Genetic and Evolutionary Computation Conference, 2021.
LibreCat
2021 | Journal Article | LibreCat-ID: 21636
J. Lienen and E. Hüllermeier, “Instance weighting through data imprecisiation,” International Journal of Approximate Reasoning, 2021.
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