[{"publication":"SIAM Journal on Scientific Computing","issue":"2","related_material":{"link":[{"url":"https://github.com/lueckem/quadrature-ML","relation":"software","description":"GitHub"}]},"abstract":[{"lang":"eng","text":"Many problems in science and engineering require an efficient numerical approximation of integrals or solutions to differential equations. For systems with rapidly changing dynamics, an equidistant discretization is often inadvisable as it results in prohibitively large errors or computational effort. To this end, adaptive schemes, such as solvers based on Runge–Kutta pairs, have been developed which adapt the step size based on local error estimations at each step. While the classical schemes apply very generally and are highly efficient on regular systems, they can behave suboptimally when an inefficient step rejection mechanism is triggered by structurally complex systems such as chaotic systems. To overcome these issues, we propose a method to tailor numerical schemes to the problem class at hand. This is achieved by combining simple, classical quadrature rules or ODE solvers with data-driven time-stepping controllers. Compared with learning solution operators to ODEs directly, it generalizes better to unseen initial data as our approach employs classical numerical schemes as base methods. At the same time it can make use of identified structures of a problem class and, therefore, outperforms state-of-the-art adaptive schemes. Several examples demonstrate superior efficiency. Source code is available at https://github.com/lueckem/quadrature-ML."}],"date_created":"2021-04-09T07:59:19Z","department":[{"_id":"101"},{"_id":"636"},{"_id":"355"},{"_id":"655"}],"type":"journal_article","author":[{"first_name":"Michael","last_name":"Dellnitz","full_name":"Dellnitz, Michael"},{"id":"48129","first_name":"Eyke","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke"},{"last_name":"Lücke","first_name":"Marvin","full_name":"Lücke, Marvin"},{"id":"16494","last_name":"Ober-Blöbaum","first_name":"Sina","full_name":"Ober-Blöbaum, Sina"},{"id":"85279","full_name":"Offen, Christian","first_name":"Christian","last_name":"Offen","orcid":"0000-0002-5940-8057"},{"id":"47427","full_name":"Peitz, Sebastian","orcid":"0000-0002-3389-793X","first_name":"Sebastian","last_name":"Peitz"},{"id":"13472","last_name":"Pfannschmidt","orcid":"0000-0001-9407-7903","first_name":"Karlson","full_name":"Pfannschmidt, Karlson"}],"year":"2023","title":"Efficient time stepping for numerical integration using reinforcement  learning","intvolume":"        45","publication_status":"published","date_updated":"2023-08-25T09:24:50Z","language":[{"iso":"eng"}],"main_file_link":[{"url":"https://epubs.siam.org/doi/reader/10.1137/21M1412682"}],"doi":"10.1137/21M1412682","citation":{"ieee":"M. Dellnitz <i>et al.</i>, “Efficient time stepping for numerical integration using reinforcement  learning,” <i>SIAM Journal on Scientific Computing</i>, vol. 45, no. 2, pp. A579–A595, 2023, doi: <a href=\"https://doi.org/10.1137/21M1412682\">10.1137/21M1412682</a>.","apa":"Dellnitz, M., Hüllermeier, E., Lücke, M., Ober-Blöbaum, S., Offen, C., Peitz, S., &#38; Pfannschmidt, K. (2023). Efficient time stepping for numerical integration using reinforcement  learning. <i>SIAM Journal on Scientific Computing</i>, <i>45</i>(2), A579–A595. <a href=\"https://doi.org/10.1137/21M1412682\">https://doi.org/10.1137/21M1412682</a>","chicago":"Dellnitz, Michael, Eyke Hüllermeier, Marvin Lücke, Sina Ober-Blöbaum, Christian Offen, Sebastian Peitz, and Karlson Pfannschmidt. “Efficient Time Stepping for Numerical Integration Using Reinforcement  Learning.” <i>SIAM Journal on Scientific Computing</i> 45, no. 2 (2023): A579–95. <a href=\"https://doi.org/10.1137/21M1412682\">https://doi.org/10.1137/21M1412682</a>.","short":"M. Dellnitz, E. Hüllermeier, M. Lücke, S. Ober-Blöbaum, C. Offen, S. Peitz, K. Pfannschmidt, SIAM Journal on Scientific Computing 45 (2023) A579–A595.","mla":"Dellnitz, Michael, et al. “Efficient Time Stepping for Numerical Integration Using Reinforcement  Learning.” <i>SIAM Journal on Scientific Computing</i>, vol. 45, no. 2, 2023, pp. A579–95, doi:<a href=\"https://doi.org/10.1137/21M1412682\">10.1137/21M1412682</a>.","bibtex":"@article{Dellnitz_Hüllermeier_Lücke_Ober-Blöbaum_Offen_Peitz_Pfannschmidt_2023, title={Efficient time stepping for numerical integration using reinforcement  learning}, volume={45}, DOI={<a href=\"https://doi.org/10.1137/21M1412682\">10.1137/21M1412682</a>}, number={2}, journal={SIAM Journal on Scientific Computing}, author={Dellnitz, Michael and Hüllermeier, Eyke and Lücke, Marvin and Ober-Blöbaum, Sina and Offen, Christian and Peitz, Sebastian and Pfannschmidt, Karlson}, year={2023}, pages={A579–A595} }","ama":"Dellnitz M, Hüllermeier E, Lücke M, et al. Efficient time stepping for numerical integration using reinforcement  learning. <i>SIAM Journal on Scientific Computing</i>. 2023;45(2):A579-A595. doi:<a href=\"https://doi.org/10.1137/21M1412682\">10.1137/21M1412682</a>"},"external_id":{"arxiv":["arXiv:2104.03562"]},"status":"public","has_accepted_license":"1","_id":"21600","page":"A579-A595","volume":45,"user_id":"47427","ddc":["510"]},{"date_created":"2023-11-10T14:17:17Z","type":"conference","department":[{"_id":"660"}],"publication":"Proceedings of the World Conference on Explainable Artificial Intelligence (xAI)","citation":{"mla":"Muschalik, Maximilian, et al. “IPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios.” <i>Proceedings of the World Conference on Explainable Artificial Intelligence (XAI)</i>, 2023, doi:<a href=\"https://doi.org/10.1007/978-3-031-44064-9_11\">10.1007/978-3-031-44064-9_11</a>.","ama":"Muschalik M, Fumagalli F, Jagtani R, Hammer B, Huellermeier E. iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios. In: <i>Proceedings of the World Conference on Explainable Artificial Intelligence (XAI)</i>. ; 2023. doi:<a href=\"https://doi.org/10.1007/978-3-031-44064-9_11\">10.1007/978-3-031-44064-9_11</a>","bibtex":"@inproceedings{Muschalik_Fumagalli_Jagtani_Hammer_Huellermeier_2023, title={iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios}, DOI={<a href=\"https://doi.org/10.1007/978-3-031-44064-9_11\">10.1007/978-3-031-44064-9_11</a>}, booktitle={Proceedings of the World Conference on Explainable Artificial Intelligence (xAI)}, author={Muschalik, Maximilian and Fumagalli, Fabian and Jagtani, Rohit and Hammer, Barbara and Huellermeier, Eyke}, year={2023} }","apa":"Muschalik, M., Fumagalli, F., Jagtani, R., Hammer, B., &#38; Huellermeier, E. (2023). iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios. <i>Proceedings of the World Conference on Explainable Artificial Intelligence (XAI)</i>. <a href=\"https://doi.org/10.1007/978-3-031-44064-9_11\">https://doi.org/10.1007/978-3-031-44064-9_11</a>","ieee":"M. Muschalik, F. Fumagalli, R. Jagtani, B. Hammer, and E. Huellermeier, “iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios,” 2023, doi: <a href=\"https://doi.org/10.1007/978-3-031-44064-9_11\">10.1007/978-3-031-44064-9_11</a>.","chicago":"Muschalik, Maximilian, Fabian Fumagalli, Rohit Jagtani, Barbara Hammer, and Eyke Huellermeier. “IPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios.” In <i>Proceedings of the World Conference on Explainable Artificial Intelligence (XAI)</i>, 2023. <a href=\"https://doi.org/10.1007/978-3-031-44064-9_11\">https://doi.org/10.1007/978-3-031-44064-9_11</a>.","short":"M. Muschalik, F. Fumagalli, R. Jagtani, B. Hammer, E. Huellermeier, in: Proceedings of the World Conference on Explainable Artificial Intelligence (XAI), 2023."},"project":[{"_id":"126","name":"TRR 318 - C3: TRR 318 - Subproject C3"},{"name":"TRR 318: TRR 318 - Erklärbarkeit konstruieren","_id":"109"},{"name":"TRR 318 - C: TRR 318 - Project Area C","_id":"117"}],"language":[{"iso":"eng"}],"_id":"48778","user_id":"93420","doi":"10.1007/978-3-031-44064-9_11","status":"public","title":"iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios","year":"2023","author":[{"full_name":"Muschalik, Maximilian","first_name":"Maximilian","last_name":"Muschalik"},{"id":"93420","full_name":"Fumagalli, Fabian","last_name":"Fumagalli","first_name":"Fabian"},{"last_name":"Jagtani","first_name":"Rohit","full_name":"Jagtani, Rohit"},{"first_name":"Barbara","last_name":"Hammer","full_name":"Hammer, Barbara"},{"full_name":"Huellermeier, Eyke","last_name":"Huellermeier","first_name":"Eyke","id":"48129"}],"publication_identifier":{"issn":["1865-0929"],"eisbn":["1865-0937"],"eissn":["9783031440649"],"isbn":["9783031440632"]},"publication_status":"published","date_updated":"2025-09-11T16:14:34Z"},{"citation":{"apa":"Muschalik, M., Fumagalli, F., Hammer, B., &#38; Huellermeier, E. (2023). iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams. In <i>Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference (ECML PKDD)</i>. Springer Nature Switzerland. <a href=\"https://doi.org/10.1007/978-3-031-43418-1_26\">https://doi.org/10.1007/978-3-031-43418-1_26</a>","ieee":"M. Muschalik, F. Fumagalli, B. Hammer, and E. Huellermeier, “iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams,” in <i>Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference (ECML PKDD)</i>, Springer Nature Switzerland, 2023.","chicago":"Muschalik, Maximilian, Fabian Fumagalli, Barbara Hammer, and Eyke Huellermeier. “ISAGE: An Incremental Version of SAGE for Online Explanation on Data Streams.” In <i>Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference (ECML PKDD)</i>. Springer Nature Switzerland, 2023. <a href=\"https://doi.org/10.1007/978-3-031-43418-1_26\">https://doi.org/10.1007/978-3-031-43418-1_26</a>.","short":"M. Muschalik, F. Fumagalli, B. Hammer, E. Huellermeier, in: Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference (ECML PKDD), Springer Nature Switzerland, 2023.","mla":"Muschalik, Maximilian, et al. “ISAGE: An Incremental Version of SAGE for Online Explanation on Data Streams.” <i>Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference (ECML PKDD)</i>, Springer Nature Switzerland, 2023, doi:<a href=\"https://doi.org/10.1007/978-3-031-43418-1_26\">10.1007/978-3-031-43418-1_26</a>.","ama":"Muschalik M, Fumagalli F, Hammer B, Huellermeier E. iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams. In: <i>Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference (ECML PKDD)</i>. Springer Nature Switzerland; 2023. doi:<a href=\"https://doi.org/10.1007/978-3-031-43418-1_26\">10.1007/978-3-031-43418-1_26</a>","bibtex":"@inbook{Muschalik_Fumagalli_Hammer_Huellermeier_2023, title={iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams}, DOI={<a href=\"https://doi.org/10.1007/978-3-031-43418-1_26\">10.1007/978-3-031-43418-1_26</a>}, booktitle={Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference (ECML PKDD)}, publisher={Springer Nature Switzerland}, author={Muschalik, Maximilian and Fumagalli, Fabian and Hammer, Barbara and Huellermeier, Eyke}, year={2023} }"},"publication":"Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference (ECML PKDD)","project":[{"_id":"126","name":"TRR 318 - C3: TRR 318 - Subproject C3"},{"name":"TRR 318 - C: TRR 318 - Project Area C","_id":"117"},{"_id":"109","name":"TRR 318: TRR 318 - Erklärbarkeit konstruieren"}],"date_created":"2023-11-10T14:11:20Z","department":[{"_id":"660"}],"type":"book_chapter","author":[{"last_name":"Muschalik","first_name":"Maximilian","full_name":"Muschalik, Maximilian"},{"full_name":"Fumagalli, Fabian","first_name":"Fabian","last_name":"Fumagalli","id":"93420"},{"full_name":"Hammer, Barbara","last_name":"Hammer","first_name":"Barbara"},{"full_name":"Huellermeier, Eyke","first_name":"Eyke","last_name":"Huellermeier","id":"48129"}],"publication_identifier":{"issn":["0302-9743"],"eissn":["1611-3349"],"isbn":["9783031434174"],"eisbn":["9783031434181"]},"status":"public","title":"iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams","year":"2023","date_updated":"2025-09-11T16:27:26Z","publication_status":"published","_id":"48776","language":[{"iso":"eng"}],"publisher":"Springer Nature Switzerland","doi":"10.1007/978-3-031-43418-1_26","user_id":"93420"},{"user_id":"93420","doi":"10.14428/ESANN/2023.ES2023-148","language":[{"iso":"eng"}],"_id":"48775","publication_status":"published","date_updated":"2025-09-11T16:26:21Z","publication_identifier":{"unknown":[" 978-2-87587-088-9"]},"author":[{"id":"93420","first_name":"Fabian","last_name":"Fumagalli","full_name":"Fumagalli, Fabian"},{"first_name":"Maximilian","last_name":"Muschalik","full_name":"Muschalik, Maximilian"},{"last_name":"Hüllermeier","first_name":"Eyke","full_name":"Hüllermeier, Eyke","id":"48129"},{"full_name":"Hammer, Barbara","first_name":"Barbara","last_name":"Hammer"}],"conference":{"name":"ESANN 2023 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning","location":"Bruges (Belgium) and online"},"title":"On Feature Removal for Explainability in Dynamic Environments","status":"public","year":"2023","department":[{"_id":"660"}],"type":"conference","date_created":"2023-11-10T14:00:08Z","project":[{"_id":"126","name":"TRR 318 - C3: TRR 318 - Subproject C3"},{"name":"TRR 318 - C: TRR 318 - Project Area C","_id":"117"},{"_id":"109","name":"TRR 318: TRR 318 - Erklärbarkeit konstruieren"}],"citation":{"chicago":"Fumagalli, Fabian, Maximilian Muschalik, Eyke Hüllermeier, and Barbara Hammer. “On Feature Removal for Explainability in Dynamic Environments.” In <i>Proceedings of the European Symposium on Artificial Neural Networks (ESANN)</i>, 2023. <a href=\"https://doi.org/10.14428/ESANN/2023.ES2023-148\">https://doi.org/10.14428/ESANN/2023.ES2023-148</a>.","short":"F. Fumagalli, M. Muschalik, E. Hüllermeier, B. Hammer, in: Proceedings of the European Symposium on Artificial Neural Networks (ESANN), 2023.","ama":"Fumagalli F, Muschalik M, Hüllermeier E, Hammer B. On Feature Removal for Explainability in Dynamic Environments. In: <i>Proceedings of the European Symposium on Artificial Neural Networks (ESANN)</i>. ; 2023. doi:<a href=\"https://doi.org/10.14428/ESANN/2023.ES2023-148\">10.14428/ESANN/2023.ES2023-148</a>","bibtex":"@inproceedings{Fumagalli_Muschalik_Hüllermeier_Hammer_2023, title={On Feature Removal for Explainability in Dynamic Environments}, DOI={<a href=\"https://doi.org/10.14428/ESANN/2023.ES2023-148\">10.14428/ESANN/2023.ES2023-148</a>}, booktitle={Proceedings of the European Symposium on Artificial Neural Networks (ESANN)}, author={Fumagalli, Fabian and Muschalik, Maximilian and Hüllermeier, Eyke and Hammer, Barbara}, year={2023} }","mla":"Fumagalli, Fabian, et al. “On Feature Removal for Explainability in Dynamic Environments.” <i>Proceedings of the European Symposium on Artificial Neural Networks (ESANN)</i>, 2023, doi:<a href=\"https://doi.org/10.14428/ESANN/2023.ES2023-148\">10.14428/ESANN/2023.ES2023-148</a>.","apa":"Fumagalli, F., Muschalik, M., Hüllermeier, E., &#38; Hammer, B. (2023). On Feature Removal for Explainability in Dynamic Environments. <i>Proceedings of the European Symposium on Artificial Neural Networks (ESANN)</i>. ESANN 2023 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges (Belgium) and online. <a href=\"https://doi.org/10.14428/ESANN/2023.ES2023-148\">https://doi.org/10.14428/ESANN/2023.ES2023-148</a>","ieee":"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: <a href=\"https://doi.org/10.14428/ESANN/2023.ES2023-148\">10.14428/ESANN/2023.ES2023-148</a>."},"publication":"Proceedings of the European Symposium on Artificial Neural Networks (ESANN)"},{"date_created":"2024-03-01T14:15:31Z","department":[{"_id":"660"}],"type":"conference","citation":{"short":"F. Fumagalli, M. Muschalik, P. Kolpaczki, E. Hüllermeier, B. Hammer, in: Advances in Neural Information Processing Systems (NeurIPS), 2023, pp. 11515--11551.","chicago":"Fumagalli, Fabian, Maximilian Muschalik, Patrick Kolpaczki, Eyke Hüllermeier, and Barbara Hammer. “SHAP-IQ: Unified Approximation of Any-Order Shapley Interactions.” In <i>Advances in Neural Information Processing Systems (NeurIPS)</i>, 36:11515--11551, 2023.","apa":"Fumagalli, F., Muschalik, M., Kolpaczki, P., Hüllermeier, E., &#38; Hammer, B. (2023). SHAP-IQ: Unified Approximation of any-order Shapley Interactions. <i>Advances in Neural Information Processing Systems (NeurIPS)</i>, <i>36</i>, 11515--11551.","ieee":"F. Fumagalli, M. Muschalik, P. Kolpaczki, E. Hüllermeier, and B. Hammer, “SHAP-IQ: Unified Approximation of any-order Shapley Interactions,” in <i>Advances in Neural Information Processing Systems (NeurIPS)</i>, 2023, vol. 36, pp. 11515--11551.","ama":"Fumagalli F, Muschalik M, Kolpaczki P, Hüllermeier E, Hammer B. SHAP-IQ: Unified Approximation of any-order Shapley Interactions. In: <i>Advances in Neural Information Processing Systems (NeurIPS)</i>. Vol 36. ; 2023:11515--11551.","bibtex":"@inproceedings{Fumagalli_Muschalik_Kolpaczki_Hüllermeier_Hammer_2023, title={SHAP-IQ: Unified Approximation of any-order Shapley Interactions}, volume={36}, booktitle={Advances in Neural Information Processing Systems (NeurIPS)}, author={Fumagalli, Fabian and Muschalik, Maximilian and Kolpaczki, Patrick and Hüllermeier, Eyke and Hammer, Barbara}, year={2023}, pages={11515--11551} }","mla":"Fumagalli, Fabian, et al. “SHAP-IQ: Unified Approximation of Any-Order Shapley Interactions.” <i>Advances in Neural Information Processing Systems (NeurIPS)</i>, vol. 36, 2023, pp. 11515--11551."},"publication":"Advances in Neural Information Processing Systems (NeurIPS)","project":[{"name":"TRR 318 - C3: TRR 318 - Subproject C3","_id":"126"},{"name":"TRR 318: TRR 318 - Erklärbarkeit konstruieren","_id":"109"},{"_id":"117","name":"TRR 318 - C: TRR 318 - Project Area C"}],"_id":"52230","language":[{"iso":"eng"}],"page":"11515--11551","volume":36,"user_id":"93420","author":[{"last_name":"Fumagalli","first_name":"Fabian","full_name":"Fumagalli, Fabian","id":"93420"},{"last_name":"Muschalik","first_name":"Maximilian","full_name":"Muschalik, Maximilian"},{"last_name":"Kolpaczki","first_name":"Patrick","full_name":"Kolpaczki, Patrick"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","last_name":"Hüllermeier","id":"48129"},{"last_name":"Hammer","first_name":"Barbara","full_name":"Hammer, Barbara"}],"status":"public","title":"SHAP-IQ: Unified Approximation of any-order Shapley Interactions","year":"2023","intvolume":"        36","date_updated":"2025-09-11T16:18:16Z"},{"project":[{"_id":"125","name":"TRR 318 - C2: TRR 318 - Subproject C2"}],"citation":{"chicago":"Hanselle, Jonas Manuel, Jaroslaw Kornowicz, Stefan Heid, Kirsten Thommes, and Eyke Hüllermeier. “Comparing Humans and Algorithms in Feature Ranking: A Case-Study in the Medical Domain.” In <i>LWDA’23: Learning, Knowledge, Data, Analysis. </i>, edited by M Leyer and J Wichmann, 2023.","short":"J.M. Hanselle, J. Kornowicz, S. Heid, K. Thommes, E. Hüllermeier, in: M. Leyer, J. Wichmann (Eds.), LWDA’23: Learning, Knowledge, Data, Analysis. , 2023.","ieee":"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 <i>LWDA’23: Learning, Knowledge, Data, Analysis. </i>, 2023.","apa":"Hanselle, J. M., Kornowicz, J., Heid, S., Thommes, K., &#38; Hüllermeier, E. (2023). Comparing Humans and Algorithms in Feature Ranking: A Case-Study in the Medical Domain. In M. Leyer &#38; J. Wichmann (Eds.), <i>LWDA’23: Learning, Knowledge, Data, Analysis. </i>.","bibtex":"@inproceedings{Hanselle_Kornowicz_Heid_Thommes_Hüllermeier_2023, title={Comparing Humans and Algorithms in Feature Ranking: A Case-Study in the Medical Domain}, booktitle={LWDA’23: Learning, Knowledge, Data, Analysis. }, author={Hanselle, Jonas Manuel and Kornowicz, Jaroslaw and Heid, Stefan and Thommes, Kirsten and Hüllermeier, Eyke}, editor={Leyer, M and Wichmann, J}, year={2023} }","ama":"Hanselle JM, Kornowicz J, Heid S, Thommes K, Hüllermeier E. Comparing Humans and Algorithms in Feature Ranking: A Case-Study in the Medical Domain. 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","department":[{"_id":"178"},{"_id":"184"}],"type":"conference","date_created":"2024-02-07T09:24:37Z","date_updated":"2024-12-09T08:09:28Z","author":[{"orcid":"0000-0002-1231-4985","first_name":"Jonas Manuel","last_name":"Hanselle","full_name":"Hanselle, Jonas Manuel","id":"43980"},{"id":"44029","full_name":"Kornowicz, Jaroslaw","orcid":"0000-0002-5654-9911","first_name":"Jaroslaw","last_name":"Kornowicz"},{"id":"39640","full_name":"Heid, Stefan","first_name":"Stefan","orcid":"0000-0002-9461-7372","last_name":"Heid"},{"id":"72497","full_name":"Thommes, Kirsten","first_name":"Kirsten","last_name":"Thommes"},{"id":"48129","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","first_name":"Eyke"}],"publication_identifier":{"issn":["1613-0073"]},"title":"Comparing Humans and Algorithms in Feature Ranking: A Case-Study in the Medical Domain","status":"public","year":"2023","editor":[{"first_name":"M","last_name":"Leyer","full_name":"Leyer, M"},{"first_name":"J","last_name":"Wichmann","full_name":"Wichmann, J"}],"user_id":"72497","_id":"51209","language":[{"iso":"eng"}],"main_file_link":[{"url":"https://ceur-ws.org/Vol-3630/LWDA2023-paper38.pdf"}]},{"date_created":"2022-04-12T12:00:08Z","external_id":{"arxiv":["2202.01651"]},"department":[{"_id":"34"},{"_id":"7"},{"_id":"26"}],"type":"preprint","citation":{"bibtex":"@article{Schede_Brandt_Tornede_Wever_Bengs_Hüllermeier_Tierney_2022, title={A Survey of Methods for Automated Algorithm Configuration}, journal={arXiv:2202.01651}, author={Schede, Elias and Brandt, Jasmin and Tornede, Alexander and Wever, Marcel Dominik and Bengs, Viktor and Hüllermeier, Eyke and Tierney, Kevin}, year={2022} }","ama":"Schede E, Brandt J, Tornede A, et al. A Survey of Methods for Automated Algorithm Configuration. <i>arXiv:220201651</i>. Published online 2022.","mla":"Schede, Elias, et al. “A Survey of Methods for Automated Algorithm Configuration.” <i>ArXiv:2202.01651</i>, 2022.","chicago":"Schede, Elias, Jasmin Brandt, Alexander Tornede, Marcel Dominik Wever, Viktor Bengs, Eyke Hüllermeier, and Kevin Tierney. “A Survey of Methods for Automated Algorithm Configuration.” <i>ArXiv:2202.01651</i>, 2022.","short":"E. Schede, J. Brandt, A. Tornede, M.D. Wever, V. Bengs, E. Hüllermeier, K. Tierney, ArXiv:2202.01651 (2022).","ieee":"E. Schede <i>et al.</i>, “A Survey of Methods for Automated Algorithm Configuration,” <i>arXiv:2202.01651</i>. 2022.","apa":"Schede, E., Brandt, J., Tornede, A., Wever, M. D., Bengs, V., Hüllermeier, E., &#38; Tierney, K. (2022). A Survey of Methods for Automated Algorithm Configuration. In <i>arXiv:2202.01651</i>."},"publication":"arXiv:2202.01651","project":[{"_id":"1","name":"SFB 901: SFB 901"},{"_id":"3","name":"SFB 901 - B: SFB 901 - Project Area B"},{"_id":"10","name":"SFB 901 - B2: SFB 901 - Subproject B2"}],"abstract":[{"lang":"eng","text":"Algorithm configuration (AC) is concerned with the automated search of the\r\nmost suitable parameter configuration of a parametrized algorithm. There is\r\ncurrently a wide variety of AC problem variants and methods proposed in the\r\nliterature. Existing reviews do not take into account all derivatives of the AC\r\nproblem, nor do they offer a complete classification scheme. To this end, we\r\nintroduce taxonomies to describe the AC problem and features of configuration\r\nmethods, respectively. We review existing AC literature within the lens of our\r\ntaxonomies, outline relevant design choices of configuration approaches,\r\ncontrast methods and problem variants against each other, and describe the\r\nstate of AC in industry. Finally, our review provides researchers and\r\npractitioners with a look at future research directions in the field of AC."}],"language":[{"iso":"eng"}],"_id":"30868","user_id":"38209","author":[{"last_name":"Schede","first_name":"Elias","full_name":"Schede, Elias"},{"last_name":"Brandt","first_name":"Jasmin","full_name":"Brandt, Jasmin"},{"full_name":"Tornede, Alexander","first_name":"Alexander","last_name":"Tornede","id":"38209"},{"id":"33176","orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik","last_name":"Wever","full_name":"Wever, Marcel Dominik"},{"id":"76599","first_name":"Viktor","last_name":"Bengs","full_name":"Bengs, Viktor"},{"full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","first_name":"Eyke","id":"48129"},{"full_name":"Tierney, Kevin","last_name":"Tierney","first_name":"Kevin"}],"year":"2022","status":"public","title":"A Survey of Methods for Automated Algorithm Configuration","date_updated":"2022-04-12T12:01:15Z"},{"project":[{"_id":"1","name":"SFB 901: SFB 901"},{"name":"SFB 901 - B: SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - B3: SFB 901 - Subproject B3","_id":"11"}],"abstract":[{"lang":"eng","text":"Testing is one of the most frequent means of quality assurance for software. Property-based testing aims at generating test suites for checking code against user-defined properties. Test input generation is, however, most often independent of the property to be checked, and is instead based on random or user-defined data generation.In this paper, we present property-driven unit testing of functions with numerical inputs and outputs. Alike property-based testing, it allows users to define the properties to be tested for. Contrary to property-based testing, it also uses the property for a targeted generation of test inputs. Our approach is a form of learning-based testing where we first of all learn a model of a given black-box function using standard machine learning algorithms, and in a second step use model and property for test input generation. This allows us to test both predefined functions as well as machine learned regression models. Our experimental evaluation shows that our property-driven approach is more effective than standard property-based testing techniques."}],"citation":{"ieee":"A. Sharma, V. Melnikov, E. Hüllermeier, and H. Wehrheim, “Property-Driven Testing of Black-Box Functions,” in <i>Proceedings of the 10th IEEE/ACM International Conference on Formal Methods in Software Engineering (FormaliSE)</i>, 2022, pp. 113–123.","mla":"Sharma, Arnab, et al. “Property-Driven Testing of Black-Box Functions.” <i>Proceedings of the 10th IEEE/ACM International Conference on Formal Methods in Software Engineering (FormaliSE)</i>, IEEE, 2022, pp. 113–23.","apa":"Sharma, A., Melnikov, V., Hüllermeier, E., &#38; Wehrheim, H. (2022). Property-Driven Testing of Black-Box Functions. <i>Proceedings of the 10th IEEE/ACM International Conference on Formal Methods in Software Engineering (FormaliSE)</i>, 113–123.","bibtex":"@inproceedings{Sharma_Melnikov_Hüllermeier_Wehrheim_2022, title={Property-Driven Testing of Black-Box Functions}, booktitle={Proceedings of the 10th IEEE/ACM International Conference on Formal Methods in Software Engineering (FormaliSE)}, publisher={IEEE}, author={Sharma, Arnab and Melnikov, Vitaly and Hüllermeier, Eyke and Wehrheim, Heike}, year={2022}, pages={113–123} }","short":"A. Sharma, V. Melnikov, E. Hüllermeier, H. Wehrheim, in: Proceedings of the 10th IEEE/ACM International Conference on Formal Methods in Software Engineering (FormaliSE), IEEE, 2022, pp. 113–123.","ama":"Sharma A, Melnikov V, Hüllermeier E, Wehrheim H. Property-Driven Testing of Black-Box Functions. In: <i>Proceedings of the 10th IEEE/ACM International Conference on Formal Methods in Software Engineering (FormaliSE)</i>. IEEE; 2022:113-123.","chicago":"Sharma, Arnab, Vitaly Melnikov, Eyke Hüllermeier, and Heike Wehrheim. “Property-Driven Testing of Black-Box Functions.” In <i>Proceedings of the 10th IEEE/ACM International Conference on Formal Methods in Software Engineering (FormaliSE)</i>, 113–23. IEEE, 2022."},"publication":"Proceedings of the 10th IEEE/ACM International Conference on Formal Methods in Software Engineering (FormaliSE)","department":[{"_id":"7"}],"type":"conference","date_created":"2022-07-01T11:18:03Z","date_updated":"2022-07-01T11:21:36Z","author":[{"first_name":"Arnab","last_name":"Sharma","full_name":"Sharma, Arnab","id":"67200"},{"first_name":"Vitaly","last_name":"Melnikov","full_name":"Melnikov, Vitaly","id":"58747"},{"last_name":"Hüllermeier","first_name":"Eyke","full_name":"Hüllermeier, Eyke","id":"48129"},{"id":"573","first_name":"Heike","last_name":"Wehrheim","full_name":"Wehrheim, Heike"}],"title":"Property-Driven Testing of Black-Box Functions","status":"public","year":"2022","user_id":"477","_id":"32311","publisher":"IEEE","language":[{"iso":"eng"}],"page":"113-123"},{"intvolume":"     13633","date_updated":"2022-12-19T09:34:44Z","author":[{"full_name":"Campagner, Andrea","first_name":"Andrea","last_name":"Campagner"},{"full_name":"Lienen, Julian","last_name":"Lienen","first_name":"Julian","id":"44040"},{"last_name":"Hüllermeier","first_name":"Eyke","full_name":"Hüllermeier, Eyke","id":"48129"},{"full_name":"Ciucci, Davide","last_name":"Ciucci","first_name":"Davide"}],"conference":{"location":"Suzhou, China","name":"International Joint Conference on Rough Sets","start_date":"2022-11-11","end_date":"2022-11-14"},"title":"Scikit-Weak: A Python Library for Weakly Supervised Machine Learning","year":"2022","status":"public","volume":13633,"user_id":"44040","_id":"34542","publisher":"Springer","language":[{"iso":"eng"}],"page":"57-70","citation":{"apa":"Campagner, A., Lienen, J., Hüllermeier, E., &#38; Ciucci, D. (2022). Scikit-Weak: A Python Library for Weakly Supervised Machine Learning. <i>Lecture Notes in Computer Science</i>, <i>13633</i>, 57–70.","ieee":"A. Campagner, J. Lienen, E. Hüllermeier, and D. Ciucci, “Scikit-Weak: A Python Library for Weakly Supervised Machine Learning,” in <i>Lecture Notes in Computer Science</i>, Suzhou, China, 2022, vol. 13633, pp. 57–70.","short":"A. Campagner, J. Lienen, E. Hüllermeier, D. Ciucci, in: Lecture Notes in Computer Science, Springer, 2022, pp. 57–70.","chicago":"Campagner, Andrea, Julian Lienen, Eyke Hüllermeier, and Davide Ciucci. “Scikit-Weak: A Python Library for Weakly Supervised Machine Learning.” In <i>Lecture Notes in Computer Science</i>, 13633:57–70. Springer, 2022.","mla":"Campagner, Andrea, et al. “Scikit-Weak: A Python Library for Weakly Supervised Machine Learning.” <i>Lecture Notes in Computer Science</i>, vol. 13633, Springer, 2022, pp. 57–70.","ama":"Campagner A, Lienen J, Hüllermeier E, Ciucci D. Scikit-Weak: A Python Library for Weakly Supervised Machine Learning. In: <i>Lecture Notes in Computer Science</i>. Vol 13633. Springer; 2022:57-70.","bibtex":"@inproceedings{Campagner_Lienen_Hüllermeier_Ciucci_2022, title={Scikit-Weak: A Python Library for Weakly Supervised Machine Learning}, volume={13633}, booktitle={Lecture Notes in Computer Science}, publisher={Springer}, author={Campagner, Andrea and Lienen, Julian and Hüllermeier, Eyke and Ciucci, Davide}, year={2022}, pages={57–70} }"},"publication":"Lecture Notes in Computer Science","type":"conference","date_created":"2022-12-19T09:34:35Z"},{"date_created":"2022-05-31T07:05:36Z","oa":"1","type":"preprint","citation":{"apa":"Lienen, J., Demir, C., &#38; Hüllermeier, E. (2022). Conformal Credal Self-Supervised Learning. In <i>arXiv:2205.15239</i>.","ieee":"J. Lienen, C. Demir, and E. Hüllermeier, “Conformal Credal Self-Supervised Learning,” <i>arXiv:2205.15239</i>. 2022.","chicago":"Lienen, Julian, Caglar Demir, and Eyke Hüllermeier. “Conformal Credal Self-Supervised Learning.” <i>ArXiv:2205.15239</i>, 2022.","short":"J. Lienen, C. Demir, E. Hüllermeier, ArXiv:2205.15239 (2022).","mla":"Lienen, Julian, et al. “Conformal Credal Self-Supervised Learning.” <i>ArXiv:2205.15239</i>, 2022.","ama":"Lienen J, Demir C, Hüllermeier E. Conformal Credal Self-Supervised Learning. <i>arXiv:220515239</i>. Published online 2022.","bibtex":"@article{Lienen_Demir_Hüllermeier_2022, title={Conformal Credal Self-Supervised Learning}, journal={arXiv:2205.15239}, author={Lienen, Julian and Demir, Caglar and Hüllermeier, Eyke}, year={2022} }"},"publication":"arXiv:2205.15239","abstract":[{"text":"In semi-supervised learning, the paradigm of self-training refers to the idea of learning from pseudo-labels suggested by the learner itself. Across various domains, corresponding methods have proven effective and achieve state-of-the-art performance. However, pseudo-labels typically stem from ad-hoc heuristics, relying on the quality of the predictions though without guaranteeing their validity. One such method, so-called credal self-supervised learning, maintains pseudo-supervision in the form of sets of (instead of single) probability distributions over labels, thereby allowing for a flexible yet uncertainty-aware labeling. Again, however, there is no justification beyond empirical effectiveness. To address this deficiency, we make use of conformal prediction, an approach that comes with guarantees on the validity of set-valued predictions. As a result, the construction of credal sets of labels is supported by a rigorous theoretical foundation, leading to better calibrated and less error-prone supervision for unlabeled data. Along with this, we present effective algorithms for learning from credal self-supervision. An empirical study demonstrates excellent calibration properties of the pseudo-supervision, as well as the competitiveness of our method on several benchmark datasets.","lang":"eng"}],"_id":"31546","language":[{"iso":"eng"}],"main_file_link":[{"url":"https://arxiv.org/abs/2205.15239","open_access":"1"}],"user_id":"44040","author":[{"id":"44040","full_name":"Lienen, Julian","last_name":"Lienen","first_name":"Julian"},{"first_name":"Caglar","last_name":"Demir","full_name":"Demir, Caglar","id":"43817"},{"full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","first_name":"Eyke","id":"48129"}],"title":"Conformal Credal Self-Supervised Learning","year":"2022","status":"public","date_updated":"2022-05-31T07:05:54Z"},{"publisher":"AAAI","_id":"30867","language":[{"iso":"eng"}],"user_id":"38209","author":[{"full_name":"Tornede, Alexander","first_name":"Alexander","last_name":"Tornede","id":"38209"},{"id":"76599","full_name":"Bengs, Viktor","last_name":"Bengs","first_name":"Viktor"},{"first_name":"Eyke","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","id":"48129"}],"title":"Machine Learning for Online Algorithm Selection under Censored Feedback","status":"public","year":"2022","date_updated":"2022-08-24T12:44:27Z","date_created":"2022-04-12T11:58:56Z","external_id":{"arxiv":["2109.06234"]},"department":[{"_id":"34"},{"_id":"7"},{"_id":"26"}],"type":"preprint","citation":{"ama":"Tornede A, Bengs V, Hüllermeier E. Machine Learning for Online Algorithm Selection under Censored Feedback. <i>Proceedings of the 36th AAAI Conference on Artificial Intelligence</i>. Published online 2022.","bibtex":"@article{Tornede_Bengs_Hüllermeier_2022, title={Machine Learning for Online Algorithm Selection under Censored Feedback}, journal={Proceedings of the 36th AAAI Conference on Artificial Intelligence}, publisher={AAAI}, author={Tornede, Alexander and Bengs, Viktor and Hüllermeier, Eyke}, year={2022} }","mla":"Tornede, Alexander, et al. “Machine Learning for Online Algorithm Selection under Censored Feedback.” <i>Proceedings of the 36th AAAI Conference on Artificial Intelligence</i>, AAAI, 2022.","chicago":"Tornede, Alexander, Viktor Bengs, and Eyke Hüllermeier. “Machine Learning for Online Algorithm Selection under Censored Feedback.” <i>Proceedings of the 36th AAAI Conference on Artificial Intelligence</i>. AAAI, 2022.","short":"A. Tornede, V. Bengs, E. Hüllermeier, Proceedings of the 36th AAAI Conference on Artificial Intelligence (2022).","apa":"Tornede, A., Bengs, V., &#38; Hüllermeier, E. (2022). Machine Learning for Online Algorithm Selection under Censored Feedback. In <i>Proceedings of the 36th AAAI Conference on Artificial Intelligence</i>. AAAI.","ieee":"A. Tornede, V. Bengs, and E. Hüllermeier, “Machine Learning for Online Algorithm Selection under Censored Feedback,” <i>Proceedings of the 36th AAAI Conference on Artificial Intelligence</i>. AAAI, 2022."},"publication":"Proceedings of the 36th AAAI Conference on Artificial Intelligence","project":[{"_id":"1","name":"SFB 901: SFB 901"},{"_id":"3","name":"SFB 901 - B: SFB 901 - Project Area B"},{"_id":"10","name":"SFB 901 - B2: SFB 901 - Subproject B2"}],"abstract":[{"text":"In online algorithm selection (OAS), instances of an algorithmic problem\r\nclass are presented to an agent one after another, and the agent has to quickly\r\nselect a presumably best algorithm from a fixed set of candidate algorithms.\r\nFor decision problems such as satisfiability (SAT), quality typically refers to\r\nthe algorithm's runtime. As the latter is known to exhibit a heavy-tail\r\ndistribution, an algorithm is normally stopped when exceeding a predefined\r\nupper time limit. As a consequence, machine learning methods used to optimize\r\nan algorithm selection strategy in a data-driven manner need to deal with\r\nright-censored samples, a problem that has received little attention in the\r\nliterature so far. In this work, we revisit multi-armed bandit algorithms for\r\nOAS and discuss their capability of dealing with the problem. Moreover, we\r\nadapt them towards runtime-oriented losses, allowing for partially censored\r\ndata while keeping a space- and time-complexity independent of the time\r\nhorizon. In an extensive experimental evaluation on an adapted version of the\r\nASlib benchmark, we demonstrate that theoretically well-founded methods based\r\non Thompson sampling perform specifically strong and improve in comparison to\r\nexisting methods.","lang":"eng"}]},{"citation":{"bibtex":"@article{Tornede_Gehring_Tornede_Wever_Hüllermeier_2022, title={Algorithm Selection on a Meta Level}, journal={Machine Learning}, author={Tornede, Alexander and Gehring, Lukas and Tornede, Tanja and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2022} }","ama":"Tornede A, Gehring L, Tornede T, Wever MD, Hüllermeier E. Algorithm Selection on a Meta Level. <i>Machine Learning</i>. Published online 2022.","mla":"Tornede, Alexander, et al. “Algorithm Selection on a Meta Level.” <i>Machine Learning</i>, 2022.","chicago":"Tornede, Alexander, Lukas Gehring, Tanja Tornede, Marcel Dominik Wever, and Eyke Hüllermeier. “Algorithm Selection on a Meta Level.” <i>Machine Learning</i>, 2022.","short":"A. Tornede, L. Gehring, T. Tornede, M.D. Wever, E. Hüllermeier, Machine Learning (2022).","ieee":"A. Tornede, L. Gehring, T. Tornede, M. D. Wever, and E. Hüllermeier, “Algorithm Selection on a Meta Level,” <i>Machine Learning</i>. 2022.","apa":"Tornede, A., Gehring, L., Tornede, T., Wever, M. D., &#38; Hüllermeier, E. (2022). Algorithm Selection on a Meta Level. In <i>Machine Learning</i>."},"publication":"Machine Learning","project":[{"name":"SFB 901: SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - B: SFB 901 - Project Area B"},{"name":"SFB 901 - B2: SFB 901 - Subproject B2","_id":"10"}],"abstract":[{"text":"The problem of selecting an algorithm that appears most suitable for a\r\nspecific instance of an algorithmic problem class, such as the Boolean\r\nsatisfiability problem, is called instance-specific algorithm selection. Over\r\nthe past decade, the problem has received considerable attention, resulting in\r\na number of different methods for algorithm selection. Although most of these\r\nmethods are based on machine learning, surprisingly little work has been done\r\non meta learning, that is, on taking advantage of the complementarity of\r\nexisting algorithm selection methods in order to combine them into a single\r\nsuperior algorithm selector. In this paper, we introduce the problem of meta\r\nalgorithm selection, which essentially asks for the best way to combine a given\r\nset of algorithm selectors. We present a general methodological framework for\r\nmeta algorithm selection as well as several concrete learning methods as\r\ninstantiations of this framework, essentially combining ideas of meta learning\r\nand ensemble learning. In an extensive experimental evaluation, we demonstrate\r\nthat ensembles of algorithm selectors can significantly outperform single\r\nalgorithm selectors and have the potential to form the new state of the art in\r\nalgorithm selection.","lang":"eng"}],"date_created":"2022-04-12T11:55:18Z","external_id":{"arxiv":["2107.09414"]},"department":[{"_id":"34"},{"_id":"7"},{"_id":"26"}],"type":"preprint","author":[{"id":"38209","full_name":"Tornede, Alexander","first_name":"Alexander","last_name":"Tornede"},{"last_name":"Gehring","first_name":"Lukas","full_name":"Gehring, Lukas"},{"id":"40795","full_name":"Tornede, Tanja","last_name":"Tornede","first_name":"Tanja"},{"id":"33176","full_name":"Wever, Marcel Dominik","last_name":"Wever","first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818"},{"id":"48129","first_name":"Eyke","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke"}],"title":"Algorithm Selection on a Meta Level","status":"public","year":"2022","date_updated":"2022-08-24T12:45:39Z","_id":"30865","language":[{"iso":"eng"}],"user_id":"38209"},{"project":[{"name":"SFB 901: SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - B: SFB 901 - Project Area B"},{"name":"SFB 901 - B2: SFB 901 - Subproject B2","_id":"10"}],"abstract":[{"text":"<jats:title>Abstract</jats:title><jats:p>Heated tool butt welding is a method often used for joining thermoplastics, especially when the components are made out of different materials. The quality of the connection between the components crucially depends on a suitable choice of the parameters of the welding process, such as heating time, temperature, and the precise way how the parts are then welded. Moreover, when different materials are to be joined, the parameter values need to be tailored to the specifics of the respective material. To this end, in this paper, three approaches to tailor the parameter values to optimize the quality of the connection are compared: a heuristic by Potente, statistical experimental design, and Bayesian optimization. With the suitability for practice in mind, a series of experiments are carried out with these approaches, and their capabilities of proposing well-performing parameter values are investigated. As a result, Bayesian optimization is found to yield peak performance, but the costs for optimization are substantial. In contrast, the Potente heuristic does not require any experimentation and recommends parameter values with competitive quality.</jats:p>","lang":"eng"}],"citation":{"mla":"Gevers, Karina, et al. “A Comparison of Heuristic, Statistical, and Machine Learning Methods for Heated Tool Butt Welding of Two Different Materials.” <i>Welding in the World</i>, Springer Science and Business Media LLC, 2022, doi:<a href=\"https://doi.org/10.1007/s40194-022-01339-9\">10.1007/s40194-022-01339-9</a>.","bibtex":"@article{Gevers_Tornede_Wever_Schöppner_Hüllermeier_2022, title={A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials}, DOI={<a href=\"https://doi.org/10.1007/s40194-022-01339-9\">10.1007/s40194-022-01339-9</a>}, journal={Welding in the World}, publisher={Springer Science and Business Media LLC}, author={Gevers, Karina and Tornede, Alexander and Wever, Marcel Dominik and Schöppner, Volker and Hüllermeier, Eyke}, year={2022} }","ama":"Gevers K, Tornede A, Wever MD, Schöppner V, Hüllermeier E. A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials. <i>Welding in the World</i>. Published online 2022. doi:<a href=\"https://doi.org/10.1007/s40194-022-01339-9\">10.1007/s40194-022-01339-9</a>","ieee":"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,” <i>Welding in the World</i>, 2022, doi: <a href=\"https://doi.org/10.1007/s40194-022-01339-9\">10.1007/s40194-022-01339-9</a>.","apa":"Gevers, K., Tornede, A., Wever, M. D., Schöppner, V., &#38; Hüllermeier, E. (2022). A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials. <i>Welding in the World</i>. <a href=\"https://doi.org/10.1007/s40194-022-01339-9\">https://doi.org/10.1007/s40194-022-01339-9</a>","short":"K. Gevers, A. Tornede, M.D. Wever, V. Schöppner, E. Hüllermeier, Welding in the World (2022).","chicago":"Gevers, Karina, Alexander Tornede, Marcel Dominik Wever, Volker Schöppner, and Eyke Hüllermeier. “A Comparison of Heuristic, Statistical, and Machine Learning Methods for Heated Tool Butt Welding of Two Different Materials.” <i>Welding in the World</i>, 2022. <a href=\"https://doi.org/10.1007/s40194-022-01339-9\">https://doi.org/10.1007/s40194-022-01339-9</a>."},"publication":"Welding in the World","type":"journal_article","keyword":["Metals and Alloys","Mechanical Engineering","Mechanics of Materials"],"date_created":"2022-08-24T12:51:07Z","publication_status":"published","date_updated":"2022-08-24T12:52:06Z","author":[{"full_name":"Gevers, Karina","first_name":"Karina","last_name":"Gevers","id":"83151"},{"id":"38209","first_name":"Alexander","last_name":"Tornede","full_name":"Tornede, Alexander"},{"id":"33176","orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","first_name":"Marcel Dominik","full_name":"Wever, Marcel Dominik"},{"id":"20530","full_name":"Schöppner, Volker","last_name":"Schöppner","first_name":"Volker"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","last_name":"Hüllermeier","id":"48129"}],"publication_identifier":{"issn":["0043-2288","1878-6669"]},"year":"2022","title":"A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials","status":"public","user_id":"38209","doi":"10.1007/s40194-022-01339-9","_id":"33090","language":[{"iso":"eng"}],"publisher":"Springer Science and Business Media LLC"},{"status":"public","year":"2022","title":"Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen Lernens","author":[{"full_name":"Hammer, Barbara","first_name":"Barbara","last_name":"Hammer"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","last_name":"Hüllermeier","id":"48129"},{"full_name":"Lohweg, Volker","last_name":"Lohweg","first_name":"Volker"},{"full_name":"Schneider, Alexander","last_name":"Schneider","first_name":"Alexander"},{"last_name":"Schenck","first_name":"Wolfram","full_name":"Schenck, Wolfram"},{"full_name":"Kuhl, Ulrike","last_name":"Kuhl","first_name":"Ulrike"},{"first_name":"Marco","last_name":"Braun","full_name":"Braun, Marco"},{"first_name":"Anton","last_name":"Pfeifer","full_name":"Pfeifer, Anton"},{"last_name":"Holst","first_name":"Christoph-Alexander","full_name":"Holst, Christoph-Alexander"},{"last_name":"Schmidt","first_name":"Malte","full_name":"Schmidt, Malte"},{"full_name":"Schomaker, Gunnar","first_name":"Gunnar","last_name":"Schomaker"},{"id":"40795","last_name":"Tornede","first_name":"Tanja","full_name":"Tornede, Tanja"}],"date_updated":"2023-01-11T15:20:40Z","has_accepted_license":"1","language":[{"iso":"ger"}],"_id":"36227","doi":"10.4119/unibi/2965622","ddc":["004"],"user_id":"40795","citation":{"apa":"Hammer, B., Hüllermeier, E., Lohweg, V., Schneider, A., Schenck, W., Kuhl, U., Braun, M., Pfeifer, A., Holst, C.-A., Schmidt, M., Schomaker, G., &#38; Tornede, T. (2022). <i>Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen Lernens</i>. <a href=\"https://doi.org/10.4119/unibi/2965622\">https://doi.org/10.4119/unibi/2965622</a>","mla":"Hammer, Barbara, et al. <i>Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen Lernens</i>. 2022, doi:<a href=\"https://doi.org/10.4119/unibi/2965622\">10.4119/unibi/2965622</a>.","ieee":"B. Hammer <i>et al.</i>, <i>Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen Lernens</i>. 2022.","short":"B. Hammer, E. Hüllermeier, V. Lohweg, A. Schneider, W. Schenck, U. Kuhl, M. Braun, A. Pfeifer, C.-A. Holst, M. Schmidt, G. Schomaker, T. Tornede, Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen Lernens, 2022.","ama":"Hammer B, Hüllermeier E, Lohweg V, et al. <i>Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen Lernens</i>.; 2022. doi:<a href=\"https://doi.org/10.4119/unibi/2965622\">10.4119/unibi/2965622</a>","chicago":"Hammer, Barbara, Eyke Hüllermeier, Volker Lohweg, Alexander Schneider, Wolfram Schenck, Ulrike Kuhl, Marco Braun, et al. <i>Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen Lernens</i>, 2022. <a href=\"https://doi.org/10.4119/unibi/2965622\">https://doi.org/10.4119/unibi/2965622</a>.","bibtex":"@book{Hammer_Hüllermeier_Lohweg_Schneider_Schenck_Kuhl_Braun_Pfeifer_Holst_Schmidt_et al._2022, title={Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen Lernens}, DOI={<a href=\"https://doi.org/10.4119/unibi/2965622\">10.4119/unibi/2965622</a>}, author={Hammer, Barbara and Hüllermeier, Eyke and Lohweg, Volker and Schneider, Alexander and Schenck, Wolfram and Kuhl, Ulrike and Braun, Marco and Pfeifer, Anton and Holst, Christoph-Alexander and Schmidt, Malte and et al.}, year={2022} }"},"date_created":"2023-01-11T15:00:00Z","type":"report","department":[{"_id":"34"},{"_id":"7"},{"_id":"534"}]},{"publisher":"Springer Science and Business Media LLC","_id":"48780","page":"211-224","volume":36,"user_id":"93420","status":"public","citation":{"short":"M. Muschalik, F. Fumagalli, B. Hammer, E. Huellermeier, KI - Künstliche Intelligenz 36 (2022) 211–224.","chicago":"Muschalik, Maximilian, Fabian Fumagalli, Barbara Hammer, and Eyke Huellermeier. “Agnostic Explanation of Model Change Based on Feature Importance.” <i>KI - Künstliche Intelligenz</i> 36, no. 3–4 (2022): 211–24. <a href=\"https://doi.org/10.1007/s13218-022-00766-6\">https://doi.org/10.1007/s13218-022-00766-6</a>.","ieee":"M. Muschalik, F. Fumagalli, B. Hammer, and E. Huellermeier, “Agnostic Explanation of Model Change based on Feature Importance,” <i>KI - Künstliche Intelligenz</i>, vol. 36, no. 3–4, pp. 211–224, 2022, doi: <a href=\"https://doi.org/10.1007/s13218-022-00766-6\">10.1007/s13218-022-00766-6</a>.","apa":"Muschalik, M., Fumagalli, F., Hammer, B., &#38; Huellermeier, E. (2022). Agnostic Explanation of Model Change based on Feature Importance. <i>KI - Künstliche Intelligenz</i>, <i>36</i>(3–4), 211–224. <a href=\"https://doi.org/10.1007/s13218-022-00766-6\">https://doi.org/10.1007/s13218-022-00766-6</a>","bibtex":"@article{Muschalik_Fumagalli_Hammer_Huellermeier_2022, title={Agnostic Explanation of Model Change based on Feature Importance}, volume={36}, DOI={<a href=\"https://doi.org/10.1007/s13218-022-00766-6\">10.1007/s13218-022-00766-6</a>}, number={3–4}, journal={KI - Künstliche Intelligenz}, publisher={Springer Science and Business Media LLC}, author={Muschalik, Maximilian and Fumagalli, Fabian and Hammer, Barbara and Huellermeier, Eyke}, year={2022}, pages={211–224} }","ama":"Muschalik M, Fumagalli F, Hammer B, Huellermeier E. Agnostic Explanation of Model Change based on Feature Importance. <i>KI - Künstliche Intelligenz</i>. 2022;36(3-4):211-224. doi:<a href=\"https://doi.org/10.1007/s13218-022-00766-6\">10.1007/s13218-022-00766-6</a>","mla":"Muschalik, Maximilian, et al. “Agnostic Explanation of Model Change Based on Feature Importance.” <i>KI - Künstliche Intelligenz</i>, vol. 36, no. 3–4, Springer Science and Business Media LLC, 2022, pp. 211–24, doi:<a href=\"https://doi.org/10.1007/s13218-022-00766-6\">10.1007/s13218-022-00766-6</a>."},"project":[{"_id":"126","name":"TRR 318 - C3: TRR 318 - Subproject C3"},{"name":"TRR 318 - C: TRR 318 - Project Area C","_id":"117"},{"grant_number":"438445824","_id":"109","name":"TRR 318: TRR 318 - Erklärbarkeit konstruieren"}],"language":[{"iso":"eng"}],"doi":"10.1007/s13218-022-00766-6","publication_identifier":{"issn":["0933-1875","1610-1987"]},"author":[{"first_name":"Maximilian","last_name":"Muschalik","full_name":"Muschalik, Maximilian"},{"full_name":"Fumagalli, Fabian","first_name":"Fabian","last_name":"Fumagalli","id":"93420"},{"first_name":"Barbara","last_name":"Hammer","full_name":"Hammer, Barbara"},{"first_name":"Eyke","last_name":"Huellermeier","full_name":"Huellermeier, Eyke","id":"48129"}],"year":"2022","title":"Agnostic Explanation of Model Change based on Feature Importance","intvolume":"        36","date_updated":"2025-01-16T16:19:35Z","publication_status":"published","date_created":"2023-11-10T14:21:06Z","department":[{"_id":"660"}],"keyword":["Artificial Intelligence"],"type":"journal_article","issue":"3-4","publication":"KI - Künstliche Intelligenz","abstract":[{"lang":"eng","text":"Explainable Artificial Intelligence (XAI) has mainly focused on static learning tasks so far. In this paper, we consider XAI in the context of online learning in dynamic environments, such as learning from real-time data streams, where models are learned incrementally and continuously adapted over the course of time. More specifically, we motivate the problem of explaining model change, i.e. explaining the difference between models before and after adaptation, instead of the models themselves. In this regard, we provide the first efficient model-agnostic approach to dynamically detecting, quantifying, and explaining significant model changes. Our approach is based on an adaptation of the well-known Permutation Feature Importance (PFI) measure. It includes two hyperparameters that control the sensitivity and directly influence explanation frequency, so that a human user can adjust the method to individual requirements and application needs. We assess and validate our method’s efficacy on illustrative synthetic data streams with three popular model classes."}]},{"user_id":"83504","language":[{"iso":"eng"}],"_id":"24143","date_updated":"2022-01-06T06:56:08Z","status":"public","title":"Automated Detection of Side Channels in Cryptographic Protocols: DROWN the ROBOTs!","year":"2021","author":[{"full_name":"Drees, Jan Peter","last_name":"Drees","first_name":"Jan Peter"},{"id":"54803","first_name":"Pritha","last_name":"Gupta","full_name":"Gupta, Pritha"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","last_name":"Hüllermeier","id":"48129"},{"full_name":"Jager, Tibor","last_name":"Jager","first_name":"Tibor"},{"first_name":"Alexander","last_name":"Konze","full_name":"Konze, Alexander"},{"full_name":"Priesterjahn, Claudia","first_name":"Claudia","last_name":"Priesterjahn"},{"id":"66937","first_name":"Arunselvan","last_name":"Ramaswamy","orcid":"https://orcid.org/ 0000-0001-7547-8111","full_name":"Ramaswamy, Arunselvan"},{"id":"83504","first_name":"Juraj","last_name":"Somorovsky","orcid":"0000-0002-3593-7720","full_name":"Somorovsky, Juraj"}],"type":"journal_article","department":[{"_id":"632"}],"date_created":"2021-09-10T09:56:27Z","publication":"14th ACM Workshop on Artificial Intelligence and Security","citation":{"mla":"Drees, Jan Peter, et al. “Automated Detection of Side Channels in Cryptographic Protocols: DROWN the ROBOTs!” <i>14th ACM Workshop on Artificial Intelligence and Security</i>, 2021.","ama":"Drees JP, Gupta P, Hüllermeier E, et al. Automated Detection of Side Channels in Cryptographic Protocols: DROWN the ROBOTs! <i>14th ACM Workshop on Artificial Intelligence and Security</i>. Published online 2021.","bibtex":"@article{Drees_Gupta_Hüllermeier_Jager_Konze_Priesterjahn_Ramaswamy_Somorovsky_2021, title={Automated Detection of Side Channels in Cryptographic Protocols: DROWN the ROBOTs!}, journal={14th ACM Workshop on Artificial Intelligence and Security}, author={Drees, Jan Peter and Gupta, Pritha and Hüllermeier, Eyke and Jager, Tibor and Konze, Alexander and Priesterjahn, Claudia and Ramaswamy, Arunselvan and Somorovsky, Juraj}, year={2021} }","apa":"Drees, J. P., Gupta, P., Hüllermeier, E., Jager, T., Konze, A., Priesterjahn, C., Ramaswamy, A., &#38; Somorovsky, J. (2021). Automated Detection of Side Channels in Cryptographic Protocols: DROWN the ROBOTs! <i>14th ACM Workshop on Artificial Intelligence and Security</i>.","ieee":"J. P. Drees <i>et al.</i>, “Automated Detection of Side Channels in Cryptographic Protocols: DROWN the ROBOTs!,” <i>14th ACM Workshop on Artificial Intelligence and Security</i>, 2021.","chicago":"Drees, Jan Peter, Pritha Gupta, Eyke Hüllermeier, Tibor Jager, Alexander Konze, Claudia Priesterjahn, Arunselvan Ramaswamy, and Juraj Somorovsky. “Automated Detection of Side Channels in Cryptographic Protocols: DROWN the ROBOTs!” <i>14th ACM Workshop on Artificial Intelligence and Security</i>, 2021.","short":"J.P. Drees, P. Gupta, E. Hüllermeier, T. Jager, A. Konze, C. Priesterjahn, A. Ramaswamy, J. Somorovsky, 14th ACM Workshop on Artificial Intelligence and Security (2021)."}},{"citation":{"mla":"Ramaswamy, Arunselvan, and Eyke Hüllermeier. “Deep Q-Learning: Theoretical Insights from an Asymptotic Analysis.” <i>IEEE Transactions on Artificial Intelligence (to Appear)</i>, 2021.","bibtex":"@article{Ramaswamy_Hüllermeier_2021, title={Deep Q-Learning: Theoretical Insights from an Asymptotic Analysis}, journal={IEEE Transactions on Artificial Intelligence (to appear)}, author={Ramaswamy, Arunselvan and Hüllermeier, Eyke}, year={2021} }","ama":"Ramaswamy A, Hüllermeier E. Deep Q-Learning: Theoretical Insights from an Asymptotic Analysis. <i>IEEE Transactions on Artificial Intelligence (to appear)</i>. Published online 2021.","ieee":"A. Ramaswamy and E. Hüllermeier, “Deep Q-Learning: Theoretical Insights from an Asymptotic Analysis,” <i>IEEE Transactions on Artificial Intelligence (to appear)</i>, 2021.","apa":"Ramaswamy, A., &#38; Hüllermeier, E. (2021). Deep Q-Learning: Theoretical Insights from an Asymptotic Analysis. <i>IEEE Transactions on Artificial Intelligence (to Appear)</i>.","short":"A. Ramaswamy, E. Hüllermeier, IEEE Transactions on Artificial Intelligence (to Appear) (2021).","chicago":"Ramaswamy, Arunselvan, and Eyke Hüllermeier. “Deep Q-Learning: Theoretical Insights from an Asymptotic Analysis.” <i>IEEE Transactions on Artificial Intelligence (to Appear)</i>, 2021."},"publication":"IEEE Transactions on Artificial Intelligence (to appear)","date_created":"2021-09-10T10:03:25Z","type":"journal_article","author":[{"last_name":"Ramaswamy","first_name":"Arunselvan","orcid":"https://orcid.org/ 0000-0001-7547-8111","full_name":"Ramaswamy, Arunselvan","id":"66937"},{"id":"48129","full_name":"Hüllermeier, Eyke","first_name":"Eyke","last_name":"Hüllermeier"}],"year":"2021","title":"Deep Q-Learning: Theoretical Insights from an Asymptotic Analysis","status":"public","date_updated":"2022-01-06T06:56:08Z","language":[{"iso":"eng"}],"_id":"24148","user_id":"66937"},{"project":[{"name":"SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - Project Area B"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"abstract":[{"text":"Automated machine learning (AutoML) supports the algorithmic construction and data-specific customization of machine learning pipelines, including the selection, combination, and parametrization of machine learning algorithms as main constituents. Generally speaking, AutoML approaches comprise two major components: a search space model and an optimizer for traversing the space. Recent approaches have shown impressive results in the realm of supervised learning, most notably (single-label) classification (SLC). Moreover, first attempts at extending these approaches towards multi-label classification (MLC) have been made. While the space of candidate pipelines is already huge in SLC, the complexity of the search space is raised to an even higher power in MLC. One may wonder, therefore, whether and to what extent optimizers established for SLC can scale to this increased complexity, and how they compare to each other. This paper makes the following contributions: First, we survey existing approaches to AutoML for MLC. Second, we augment these approaches with optimizers not previously tried for MLC. Third, we propose a benchmarking framework that supports a fair and systematic comparison. Fourth, we conduct an extensive experimental study, evaluating the methods on a suite of MLC problems. We find a grammar-based best-first search to compare favorably to other optimizers.","lang":"eng"}],"citation":{"ieee":"M. D. Wever, A. Tornede, F. Mohr, and E. Hüllermeier, “AutoML for Multi-Label Classification: Overview and Empirical Evaluation,” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, pp. 1–1, 2021, doi: <a href=\"https://doi.org/10.1109/tpami.2021.3051276\">10.1109/tpami.2021.3051276</a>.","apa":"Wever, M. D., Tornede, A., Mohr, F., &#38; Hüllermeier, E. (2021). AutoML for Multi-Label Classification: Overview and Empirical Evaluation. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, 1–1. <a href=\"https://doi.org/10.1109/tpami.2021.3051276\">https://doi.org/10.1109/tpami.2021.3051276</a>","mla":"Wever, Marcel Dominik, et al. “AutoML for Multi-Label Classification: Overview and Empirical Evaluation.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, 2021, pp. 1–1, doi:<a href=\"https://doi.org/10.1109/tpami.2021.3051276\">10.1109/tpami.2021.3051276</a>.","bibtex":"@article{Wever_Tornede_Mohr_Hüllermeier_2021, title={AutoML for Multi-Label Classification: Overview and Empirical Evaluation}, DOI={<a href=\"https://doi.org/10.1109/tpami.2021.3051276\">10.1109/tpami.2021.3051276</a>}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, author={Wever, Marcel Dominik and Tornede, Alexander and Mohr, Felix and Hüllermeier, Eyke}, year={2021}, pages={1–1} }","ama":"Wever MD, Tornede A, Mohr F, Hüllermeier E. AutoML for Multi-Label Classification: Overview and Empirical Evaluation. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. Published online 2021:1-1. doi:<a href=\"https://doi.org/10.1109/tpami.2021.3051276\">10.1109/tpami.2021.3051276</a>","short":"M.D. Wever, A. Tornede, F. Mohr, E. Hüllermeier, IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) 1–1.","chicago":"Wever, Marcel Dominik, Alexander Tornede, Felix Mohr, and Eyke Hüllermeier. “AutoML for Multi-Label Classification: Overview and Empirical Evaluation.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, 2021, 1–1. <a href=\"https://doi.org/10.1109/tpami.2021.3051276\">https://doi.org/10.1109/tpami.2021.3051276</a>."},"publication":"IEEE Transactions on Pattern Analysis and Machine Intelligence","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"keyword":["Automated Machine Learning","Multi Label Classification","Hierarchical Planning","Bayesian Optimization"],"type":"journal_article","date_created":"2021-01-16T14:48:13Z","publication_status":"published","date_updated":"2022-01-06T06:54:42Z","publication_identifier":{"issn":["0162-8828","2160-9292","1939-3539"]},"author":[{"full_name":"Wever, Marcel Dominik","last_name":"Wever","first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","id":"33176"},{"full_name":"Tornede, Alexander","first_name":"Alexander","last_name":"Tornede","id":"38209"},{"full_name":"Mohr, Felix","first_name":"Felix","last_name":"Mohr"},{"id":"48129","last_name":"Hüllermeier","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}],"title":"AutoML for Multi-Label Classification: Overview and Empirical Evaluation","status":"public","year":"2021","user_id":"5786","doi":"10.1109/tpami.2021.3051276","language":[{"iso":"eng"}],"_id":"21004","page":"1-1"},{"_id":"21092","publisher":"IEEE","language":[{"iso":"eng"}],"user_id":"5786","author":[{"first_name":"Felix","last_name":"Mohr","full_name":"Mohr, Felix"},{"full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","first_name":"Marcel Dominik","id":"33176"},{"id":"38209","full_name":"Tornede, Alexander","first_name":"Alexander","last_name":"Tornede"},{"id":"48129","first_name":"Eyke","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke"}],"title":"Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning","year":"2021","status":"public","publication_status":"accepted","date_updated":"2022-01-06T06:54:45Z","date_created":"2021-01-27T13:45:52Z","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"type":"journal_article","citation":{"ama":"Mohr F, Wever MD, Tornede A, Hüllermeier E. Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>.","bibtex":"@article{Mohr_Wever_Tornede_Hüllermeier, title={Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, publisher={IEEE}, author={Mohr, Felix and Wever, Marcel Dominik and Tornede, Alexander and Hüllermeier, Eyke} }","mla":"Mohr, Felix, et al. “Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, IEEE.","chicago":"Mohr, Felix, Marcel Dominik Wever, Alexander Tornede, and Eyke Hüllermeier. “Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, n.d.","short":"F. Mohr, M.D. Wever, A. Tornede, E. Hüllermeier, IEEE Transactions on Pattern Analysis and Machine Intelligence (n.d.).","apa":"Mohr, F., Wever, M. D., Tornede, A., &#38; Hüllermeier, E. (n.d.). Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>.","ieee":"F. Mohr, M. D. Wever, A. Tornede, and E. Hüllermeier, “Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning,” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>."},"publication":"IEEE Transactions on Pattern Analysis and Machine Intelligence","project":[{"name":"SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - Project Area B"},{"name":"SFB 901 - Subproject B2","_id":"10"},{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"abstract":[{"text":"Automated Machine Learning (AutoML) seeks to automatically find so-called machine learning pipelines that maximize the prediction performance when being used to train a model on a given dataset. One of the main and yet open challenges in AutoML is an effective use of computational resources: An AutoML process involves the evaluation of many candidate pipelines, which   are costly but often ineffective because they are canceled due to a timeout.\r\nIn this paper, we present an approach to predict the runtime of two-step machine learning pipelines with up to one pre-processor, which can be used to anticipate whether or not a pipeline will time out. Separate runtime models are trained offline for each algorithm that may be used in a pipeline, and an overall prediction is derived from these models. We empirically show that the approach increases successful evaluations made by an AutoML tool while preserving or even improving on the previously best solutions.","lang":"eng"}]},{"project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - Subproject B2","_id":"10"},{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"publication":"Proceedings of the Genetic and Evolutionary Computation Conference","citation":{"mla":"Tornede, Tanja, et al. “Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2021.","bibtex":"@inproceedings{Tornede_Tornede_Wever_Hüllermeier_2021, title={Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, author={Tornede, Tanja and Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2021} }","ama":"Tornede T, Tornede A, Wever MD, Hüllermeier E. Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. ; 2021.","ieee":"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.","apa":"Tornede, T., Tornede, A., Wever, M. D., &#38; Hüllermeier, E. (2021). Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance. <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. Genetic and Evolutionary Computation Conference.","short":"T. Tornede, A. Tornede, M.D. Wever, E. Hüllermeier, in: Proceedings of the Genetic and Evolutionary Computation Conference, 2021.","chicago":"Tornede, Tanja, Alexander Tornede, Marcel Dominik Wever, and Eyke Hüllermeier. “Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2021."},"type":"conference","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"date_created":"2021-03-26T09:14:19Z","date_updated":"2022-01-06T06:55:06Z","title":"Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance","status":"public","year":"2021","author":[{"first_name":"Tanja","last_name":"Tornede","full_name":"Tornede, Tanja","id":"40795"},{"id":"38209","last_name":"Tornede","first_name":"Alexander","full_name":"Tornede, Alexander"},{"last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik","full_name":"Wever, Marcel Dominik","id":"33176"},{"last_name":"Hüllermeier","first_name":"Eyke","full_name":"Hüllermeier, Eyke","id":"48129"}],"conference":{"start_date":"2021-07-10","name":"Genetic and Evolutionary Computation Conference","end_date":"2021-07-14"},"user_id":"5786","_id":"21570","language":[{"iso":"eng"}]}]