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439 Publications
2024 | Journal Article | LibreCat-ID: 53073
Muschalik, Maximilian, et al. “Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles.” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 13, Association for the Advancement of Artificial Intelligence (AAAI), 2024, pp. 14388–96, doi:10.1609/aaai.v38i13.29352.
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| DOI
2023 | Preprint | LibreCat-ID: 44512 | 
Uhlemeyer, Svenja, et al. “Detecting Novelties with Empty Classes.” ArXiv:2305.00983, 2023.
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| arXiv
2023 | Conference Paper | LibreCat-ID: 31880 |
Nguyen, Duc Anh, et al. “Memorization-Dilation: Modeling Neural Collapse Under Noise.” International Conference on Learning Representations, ICLR, 2023.
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2023 | Book Chapter | LibreCat-ID: 45884 |
Hanselle, Jonas Manuel, et al. “Configuration and Evaluation.” On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets, edited by Claus-Jochen Haake et al., vol. 412, Heinz Nixdorf Institut, Universität Paderborn, 2023, pp. 85–104, doi:10.5281/zenodo.8068466.
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2023 | Book Chapter | LibreCat-ID: 45886 |
Wehrheim, Heike, et al. “Composition Analysis in Unknown Contexts.” On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets, edited by Claus-Jochen Haake et al., vol. 412, Heinz Nixdorf Institut, Universität Paderborn, 2023, pp. 105–23, doi:10.5281/zenodo.8068510.
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2023 | Preprint | LibreCat-ID: 45911 |
Lienen, Julian, and Eyke Hüllermeier. “Mitigating Label Noise through Data Ambiguation.” ArXiv:2305.13764, 2023.
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| arXiv
2023 | Journal Article | LibreCat-ID: 21600
Dellnitz, Michael, et al. “Efficient Time Stepping for Numerical Integration Using Reinforcement Learning.” SIAM Journal on Scientific Computing, vol. 45, no. 2, 2023, pp. A579–95, doi:10.1137/21M1412682.
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| arXiv
2023 | Conference Paper | LibreCat-ID: 51373
Hanselle, Jonas Manuel, et al. “Probabilistic Scoring Lists for Interpretable Machine Learning.” 26th International Conference on Discovery Science , vol. 14050, Springer Nature Switzerland, 2023, pp. 189–203, doi:10.1007/978-3-031-45275-8_13.
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2023 | Book Chapter | LibreCat-ID: 48776
Muschalik, Maximilian, et al. “ISAGE: An Incremental Version of SAGE for Online Explanation on Data Streams.” Machine Learning and Knowledge Discovery in Databases: Research Track, Springer Nature Switzerland, 2023, doi:10.1007/978-3-031-43418-1_26.
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2023 | Book Chapter | LibreCat-ID: 48778
Muschalik, Maximilian, et al. “IPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios.” Communications in Computer and Information Science, Springer Nature Switzerland, 2023, doi:10.1007/978-3-031-44064-9_11.
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2023 | Conference Paper | LibreCat-ID: 48775
Fumagalli, Fabian, et al. “On Feature Removal for Explainability in Dynamic Environments.” ESANN 2023 Proceedings, i6doc.com publ., 2023, doi:10.14428/esann/2023.es2023-148.
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2023 | Conference Paper | LibreCat-ID: 52230
Fumagalli, Fabian, et al. “SHAP-IQ: Unified Approximation of Any-Order Shapley Interactions.” NeurIPS 2023 - Advances in Neural Information Processing Systems, vol. 36, Curran Associates, Inc., 2023, pp. 11515--11551.
LibreCat
2022 | Conference Paper | LibreCat-ID: 32311
Sharma, Arnab, et al. “Property-Driven Testing of Black-Box Functions.” Proceedings of the 10th IEEE/ACM International Conference on Formal Methods in Software Engineering (FormaliSE), IEEE, 2022, pp. 113–23.
LibreCat
2022 | Conference Paper | LibreCat-ID: 34542
Campagner, Andrea, et al. “Scikit-Weak: A Python Library for Weakly Supervised Machine Learning.” Lecture Notes in Computer Science, vol. 13633, Springer, 2022, pp. 57–70.
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2022 | Preprint | LibreCat-ID: 31546 |
Lienen, Julian, et al. “Conformal Credal Self-Supervised Learning.” ArXiv:2205.15239, 2022.
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2022 | Journal Article | LibreCat-ID: 33090
Gevers, Karina, et al. “A Comparison of Heuristic, Statistical, and Machine Learning Methods for Heated Tool Butt Welding of Two Different Materials.” Welding in the World, Springer Science and Business Media LLC, 2022, doi:10.1007/s40194-022-01339-9.
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2022 | Report | LibreCat-ID: 36227
Hammer, Barbara, et al. Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen Lernens. 2022, doi:10.4119/unibi/2965622.
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