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448 Publications
2023 | Preprint | LibreCat-ID: 45911 | 
Lienen, J., & Hüllermeier, E. (2023). Mitigating Label Noise through Data Ambiguation. In arXiv:2305.13764.
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| arXiv
2023 | Journal Article | LibreCat-ID: 21600
Dellnitz, M., Hüllermeier, E., Lücke, M., Ober-Blöbaum, S., Offen, C., Peitz, S., & Pfannschmidt, K. (2023). Efficient time stepping for numerical integration using reinforcement learning. SIAM Journal on Scientific Computing, 45(2), A579–A595. https://doi.org/10.1137/21M1412682
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| arXiv
2023 | Book Chapter | LibreCat-ID: 48776
Muschalik, M., Fumagalli, F., Hammer, B., & Huellermeier, E. (2023). iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams. In Machine Learning and Knowledge Discovery in Databases: Research Track. Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-43418-1_26
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2023 | Conference Paper | LibreCat-ID: 48775
Fumagalli, F., Muschalik, M., Hüllermeier, E., & Hammer, B. (2023). On Feature Removal for Explainability in Dynamic Environments. ESANN 2023 Proceedings. ESANN 2023 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges (Belgium) and online. https://doi.org/10.14428/esann/2023.es2023-148
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2023 | Book Chapter | LibreCat-ID: 48778
Muschalik, M., Fumagalli, F., Jagtani, R., Hammer, B., & Huellermeier, E. (2023). iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios. In Communications in Computer and Information Science. Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-44064-9_11
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2023 | Conference Paper | LibreCat-ID: 51209
Hanselle, J. M., Kornowicz, J., Heid, S., Thommes, K., & Hüllermeier, E. (2023). Comparing Humans and Algorithms in Feature Ranking: A Case-Study in the Medical Domain. In M. Leyer & J. Wichmann (Eds.), LWDA’23: Learning, Knowledge, Data, Analysis. .
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2022 | Conference Paper | LibreCat-ID: 32311
Sharma, A., Melnikov, V., Hüllermeier, E., & Wehrheim, H. (2022). Property-Driven Testing of Black-Box Functions. Proceedings of the 10th IEEE/ACM International Conference on Formal Methods in Software Engineering (FormaliSE), 113–123.
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2022 | Conference Paper | LibreCat-ID: 34542
Campagner, A., Lienen, J., Hüllermeier, E., & Ciucci, D. (2022). Scikit-Weak: A Python Library for Weakly Supervised Machine Learning. Lecture Notes in Computer Science, 13633, 57–70.
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2022 | Preprint | LibreCat-ID: 31546 |
Lienen, J., Demir, C., & Hüllermeier, E. (2022). Conformal Credal Self-Supervised Learning. In arXiv:2205.15239.
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2022 | Journal Article | LibreCat-ID: 33090
Gevers, K., Tornede, A., Wever, M. D., Schöppner, V., & Hüllermeier, E. (2022). A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials. Welding in the World. https://doi.org/10.1007/s40194-022-01339-9
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2022 | Report | LibreCat-ID: 36227
Hammer, B., Hüllermeier, E., Lohweg, V., Schneider, A., Schenck, W., Kuhl, U., Braun, M., Pfeifer, A., Holst, C.-A., Schmidt, M., Schomaker, G., & Tornede, T. (2022). Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen Lernens. https://doi.org/10.4119/unibi/2965622
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2022 | Journal Article | LibreCat-ID: 48780
Muschalik, M., Fumagalli, F., Hammer, B., & Huellermeier, E. (2022). Agnostic Explanation of Model Change based on Feature Importance. KI - Künstliche Intelligenz, 36(3–4), 211–224. https://doi.org/10.1007/s13218-022-00766-6
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2021 | Journal Article | LibreCat-ID: 24143
Drees, J. P., Gupta, P., Hüllermeier, E., Jager, T., Konze, A., Priesterjahn, C., Ramaswamy, A., & Somorovsky, J. (2021). Automated Detection of Side Channels in Cryptographic Protocols: DROWN the ROBOTs! 14th ACM Workshop on Artificial Intelligence and Security.
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2021 | Journal Article | LibreCat-ID: 24148
Ramaswamy, A., & Hüllermeier, E. (2021). Deep Q-Learning: Theoretical Insights from an Asymptotic Analysis. IEEE Transactions on Artificial Intelligence (to Appear).
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2021 | Journal Article | LibreCat-ID: 21004
Wever, M. D., Tornede, A., Mohr, F., & Hüllermeier, E. (2021). AutoML for Multi-Label Classification: Overview and Empirical Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/tpami.2021.3051276
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2021 | Journal Article | LibreCat-ID: 21092
Mohr, F., Wever, M. D., Tornede, A., & Hüllermeier, E. (n.d.). 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
Tornede, T., Tornede, A., Wever, M. D., & Hüllermeier, E. (2021). Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance. Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference.
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