[{"date_updated":"2023-08-04T06:01:49Z","doi":"10.17619/UNIPB/1-1780 ","oa":"1","language":[{"iso":"eng"}],"title":"Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions","department":[{"_id":"355"}],"project":[{"_id":"10","name":"SFB 901 - B2: Konfiguration und Bewertung (B02)","grant_number":"160364472"},{"_id":"3","name":"SFB 901 - B: SFB 901 - Project Area B"},{"_id":"1","grant_number":"160364472","name":"SFB 901: SFB 901: On-The-Fly Computing - Individualisierte IT-Dienstleistungen in dynamischen Märkten "}],"_id":"45780","year":"2023","type":"dissertation","citation":{"mla":"Tornede, Alexander. Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions. 2023, doi:10.17619/UNIPB/1-1780 .","bibtex":"@book{Tornede_2023, title={Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions}, DOI={10.17619/UNIPB/1-1780 }, author={Tornede, Alexander}, year={2023} }","ieee":"A. Tornede, Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions. 2023.","chicago":"Tornede, Alexander. Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions, 2023. https://doi.org/10.17619/UNIPB/1-1780 .","ama":"Tornede A. Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions.; 2023. doi:10.17619/UNIPB/1-1780 ","short":"A. Tornede, Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions, 2023.","apa":"Tornede, A. (2023). Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions. https://doi.org/10.17619/UNIPB/1-1780 "},"supervisor":[{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","last_name":"Hüllermeier"}],"ddc":["006"],"user_id":"15504","file_date_updated":"2023-07-24T08:42:01Z","author":[{"full_name":"Tornede, Alexander","first_name":"Alexander","id":"38209","last_name":"Tornede"}],"file":[{"file_size":4300633,"creator":"ahetzer","file_id":"46118","title":" Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions","date_updated":"2023-07-24T08:42:01Z","content_type":"application/pdf","relation":"main_file","date_created":"2023-07-24T08:40:35Z","file_name":"dissertation_alexander_tornede_final_publishing_compressed.pdf","access_level":"open_access"}],"date_created":"2023-06-27T05:20:14Z","has_accepted_license":"1","status":"public"},{"user_id":"47427","ddc":["510"],"abstract":[{"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.","lang":"eng"}],"status":"public","has_accepted_license":"1","date_created":"2021-04-09T07:59:19Z","volume":45,"author":[{"full_name":"Dellnitz, Michael","first_name":"Michael","last_name":"Dellnitz"},{"id":"48129","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"},{"first_name":"Marvin","full_name":"Lücke, Marvin","last_name":"Lücke"},{"full_name":"Ober-Blöbaum, Sina","first_name":"Sina","id":"16494","last_name":"Ober-Blöbaum"},{"first_name":"Christian","orcid":"0000-0002-5940-8057","full_name":"Offen, Christian","last_name":"Offen","id":"85279"},{"first_name":"Sebastian","orcid":"0000-0002-3389-793X","full_name":"Peitz, Sebastian","last_name":"Peitz","id":"47427"},{"orcid":"0000-0001-9407-7903","full_name":"Pfannschmidt, Karlson","first_name":"Karlson","id":"13472","last_name":"Pfannschmidt"}],"publication":"SIAM Journal on Scientific Computing","issue":"2","intvolume":" 45","_id":"21600","year":"2023","citation":{"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.” SIAM Journal on Scientific Computing 45, no. 2 (2023): A579–95. https://doi.org/10.1137/21M1412682.","ama":"Dellnitz M, Hüllermeier E, Lücke M, et al. Efficient time stepping for numerical integration using reinforcement learning. SIAM Journal on Scientific Computing. 2023;45(2):A579-A595. doi:10.1137/21M1412682","apa":"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","mla":"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.","bibtex":"@article{Dellnitz_Hüllermeier_Lücke_Ober-Blöbaum_Offen_Peitz_Pfannschmidt_2023, title={Efficient time stepping for numerical integration using reinforcement learning}, volume={45}, DOI={10.1137/21M1412682}, 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} }","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.","ieee":"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."},"type":"journal_article","page":"A579-A595","main_file_link":[{"url":"https://epubs.siam.org/doi/reader/10.1137/21M1412682"}],"related_material":{"link":[{"description":"GitHub","relation":"software","url":"https://github.com/lueckem/quadrature-ML"}]},"title":"Efficient time stepping for numerical integration using reinforcement learning","external_id":{"arxiv":["arXiv:2104.03562"]},"publication_status":"published","department":[{"_id":"101"},{"_id":"636"},{"_id":"355"},{"_id":"655"}],"doi":"10.1137/21M1412682","date_updated":"2023-08-25T09:24:50Z","language":[{"iso":"eng"}]},{"user_id":"83151","title":"Heated tool butt welding of two different materials – Established methods versus artificial intelligence","department":[{"_id":"367"},{"_id":"355"},{"_id":"321"}],"author":[{"last_name":"Gevers","id":"83151","first_name":"Karina","full_name":"Gevers, Karina"},{"full_name":"Schöppner, Volker","first_name":"Volker","id":"20530","last_name":"Schöppner"},{"last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"date_created":"2021-09-14T11:34:31Z","status":"public","conference":{"end_date":"2021-07-14","start_date":"2021-07-12","name":"International Institute of Welding","location":"online"},"_id":"24382","date_updated":"2022-01-06T06:56:19Z","language":[{"iso":"eng"}],"year":"2021","type":"conference","citation":{"short":"K. Gevers, V. Schöppner, E. Hüllermeier, in: 2021.","ieee":"K. Gevers, V. Schöppner, and E. Hüllermeier, “Heated tool butt welding of two different materials – Established methods versus artificial intelligence,” presented at the International Institute of Welding, online, 2021.","apa":"Gevers, K., Schöppner, V., & Hüllermeier, E. (2021). Heated tool butt welding of two different materials – Established methods versus artificial intelligence. International Institute of Welding, online.","ama":"Gevers K, Schöppner V, Hüllermeier E. Heated tool butt welding of two different materials – Established methods versus artificial intelligence. In: ; 2021.","chicago":"Gevers, Karina, Volker Schöppner, and Eyke Hüllermeier. “Heated Tool Butt Welding of Two Different Materials – Established Methods versus Artificial Intelligence,” 2021.","bibtex":"@inproceedings{Gevers_Schöppner_Hüllermeier_2021, title={Heated tool butt welding of two different materials – Established methods versus artificial intelligence}, author={Gevers, Karina and Schöppner, Volker and Hüllermeier, Eyke}, year={2021} }","mla":"Gevers, Karina, et al. Heated Tool Butt Welding of Two Different Materials – Established Methods versus Artificial Intelligence. 2021."}},{"author":[{"last_name":"Wever","id":"33176","first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik"},{"full_name":"Tornede, Alexander","first_name":"Alexander","id":"38209","last_name":"Tornede"},{"last_name":"Mohr","full_name":"Mohr, Felix","first_name":"Felix"},{"id":"48129","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"publication":"IEEE Transactions on Pattern Analysis and Machine Intelligence","keyword":["Automated Machine Learning","Multi Label Classification","Hierarchical Planning","Bayesian Optimization"],"department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"status":"public","project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Project Area B","_id":"3"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"date_created":"2021-01-16T14:48:13Z","publication_status":"published","publication_identifier":{"issn":["0162-8828","2160-9292","1939-3539"]},"abstract":[{"lang":"eng","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."}],"user_id":"5786","title":"AutoML for Multi-Label Classification: Overview and Empirical Evaluation","language":[{"iso":"eng"}],"type":"journal_article","citation":{"apa":"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","ama":"Wever MD, Tornede A, Mohr F, Hüllermeier E. AutoML for Multi-Label Classification: Overview and Empirical Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence. Published online 2021:1-1. doi:10.1109/tpami.2021.3051276","chicago":"Wever, Marcel Dominik, Alexander Tornede, Felix Mohr, and Eyke Hüllermeier. “AutoML for Multi-Label Classification: Overview and Empirical Evaluation.” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 1–1. https://doi.org/10.1109/tpami.2021.3051276.","bibtex":"@article{Wever_Tornede_Mohr_Hüllermeier_2021, title={AutoML for Multi-Label Classification: Overview and Empirical Evaluation}, DOI={10.1109/tpami.2021.3051276}, 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} }","mla":"Wever, Marcel Dominik, et al. “AutoML for Multi-Label Classification: Overview and Empirical Evaluation.” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, pp. 1–1, doi:10.1109/tpami.2021.3051276.","short":"M.D. Wever, A. Tornede, F. Mohr, E. Hüllermeier, IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) 1–1.","ieee":"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."},"year":"2021","page":"1-1","date_updated":"2022-01-06T06:54:42Z","_id":"21004","doi":"10.1109/tpami.2021.3051276"},{"title":"Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning","user_id":"5786","abstract":[{"lang":"eng","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."}],"publication_status":"accepted","project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Project Area B","_id":"3"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"date_created":"2021-01-27T13:45:52Z","status":"public","publication":"IEEE Transactions on Pattern Analysis and Machine Intelligence","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"author":[{"last_name":"Mohr","first_name":"Felix","full_name":"Mohr, Felix"},{"orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik","first_name":"Marcel Dominik","id":"33176","last_name":"Wever"},{"id":"38209","last_name":"Tornede","full_name":"Tornede, Alexander","first_name":"Alexander"},{"id":"48129","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"publisher":"IEEE","_id":"21092","date_updated":"2022-01-06T06:54:45Z","type":"journal_article","citation":{"short":"F. Mohr, M.D. Wever, A. Tornede, E. Hüllermeier, IEEE Transactions on Pattern Analysis and Machine Intelligence (n.d.).","ieee":"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.","ama":"Mohr F, Wever MD, Tornede A, Hüllermeier E. Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence.","apa":"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.","chicago":"Mohr, Felix, Marcel Dominik Wever, Alexander Tornede, and Eyke Hüllermeier. “Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning.” IEEE Transactions on Pattern Analysis and Machine Intelligence, n.d.","mla":"Mohr, Felix, et al. “Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning.” IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE.","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} }"},"year":"2021","language":[{"iso":"eng"}]},{"language":[{"iso":"eng"}],"page":"1-108","type":"journal_article","citation":{"ieee":"V. Bengs, R. Busa-Fekete, A. El Mesaoudi-Paul, and E. Hüllermeier, “Preference-based Online Learning with Dueling Bandits: A Survey,” Journal of Machine Learning Research, vol. 22, no. 7, pp. 1–108, 2021.","short":"V. Bengs, R. Busa-Fekete, A. El Mesaoudi-Paul, E. Hüllermeier, Journal of Machine Learning Research 22 (2021) 1–108.","bibtex":"@article{Bengs_Busa-Fekete_El Mesaoudi-Paul_Hüllermeier_2021, title={Preference-based Online Learning with Dueling Bandits: A Survey}, volume={22}, number={7}, journal={Journal of Machine Learning Research}, author={Bengs, Viktor and Busa-Fekete, Róbert and El Mesaoudi-Paul, Adil and Hüllermeier, Eyke}, year={2021}, pages={1–108} }","mla":"Bengs, Viktor, et al. “Preference-Based Online Learning with Dueling Bandits: A Survey.” Journal of Machine Learning Research, vol. 22, no. 7, 2021, pp. 1–108.","chicago":"Bengs, Viktor, Róbert Busa-Fekete, Adil El Mesaoudi-Paul, and Eyke Hüllermeier. “Preference-Based Online Learning with Dueling Bandits: A Survey.” Journal of Machine Learning Research 22, no. 7 (2021): 1–108.","ama":"Bengs V, Busa-Fekete R, El Mesaoudi-Paul A, Hüllermeier E. Preference-based Online Learning with Dueling Bandits: A Survey. Journal of Machine Learning Research. 2021;22(7):1-108.","apa":"Bengs, V., Busa-Fekete, R., El Mesaoudi-Paul, A., & Hüllermeier, E. (2021). Preference-based Online Learning with Dueling Bandits: A Survey. Journal of Machine Learning Research, 22(7), 1–108."},"year":"2021","intvolume":" 22","_id":"21535","date_updated":"2022-01-06T06:55:03Z","issue":"7","publication":"Journal of Machine Learning Research","department":[{"_id":"34"},{"_id":"7"},{"_id":"355"}],"quality_controlled":"1","author":[{"last_name":"Bengs","full_name":"Bengs, Viktor","first_name":"Viktor"},{"last_name":"Busa-Fekete","full_name":"Busa-Fekete, Róbert","first_name":"Róbert"},{"last_name":"El Mesaoudi-Paul","first_name":"Adil","full_name":"El Mesaoudi-Paul, Adil"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier"}],"date_created":"2021-03-18T11:15:38Z","status":"public","volume":22,"user_id":"76599","title":"Preference-based Online Learning with Dueling Bandits: A Survey"},{"year":"2021","type":"conference","citation":{"short":"T. Tornede, A. Tornede, M.D. Wever, E. Hüllermeier, in: Proceedings of the Genetic and Evolutionary Computation Conference, 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., & 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.","ama":"Tornede T, Tornede A, Wever MD, Hüllermeier E. Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance. 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 Proceedings of the Genetic and Evolutionary Computation Conference, 2021.","mla":"Tornede, Tanja, et al. “Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance.” Proceedings of the Genetic and Evolutionary Computation Conference, 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} }"},"language":[{"iso":"eng"}],"conference":{"end_date":"2021-07-14","name":"Genetic and Evolutionary Computation Conference","start_date":"2021-07-10"},"_id":"21570","date_updated":"2022-01-06T06:55:06Z","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"publication":"Proceedings of the Genetic and Evolutionary Computation Conference","author":[{"first_name":"Tanja","full_name":"Tornede, Tanja","last_name":"Tornede","id":"40795"},{"first_name":"Alexander","full_name":"Tornede, Alexander","last_name":"Tornede","id":"38209"},{"id":"33176","last_name":"Wever","full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik"},{"id":"48129","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - Subproject B2","_id":"10"},{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"date_created":"2021-03-26T09:14:19Z","status":"public","title":"Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance","user_id":"5786"},{"conference":{"location":"Montreal, Kanada","name":"30th International Joint Conference on Artificial Intelligence (IJCAI 2021) - Workshop \"AI and Product Design\"","start_date":"2021-08-19","end_date":"2021-08-26"},"date_updated":"2022-01-06T06:55:59Z","_id":"23779","main_file_link":[{"url":"https://www.hsu-hh.de/imb/wp-content/uploads/sites/677/2021/08/A-Meta-Review-on-Artificial-Intelligence-in-Product-Creation.pdf"}],"year":"2021","type":"conference","citation":{"short":"R. Bernijazov, A. Dicks, R. Dumitrescu, M. Foullois, J.M. Hanselle, E. Hüllermeier, G. Karakaya, P. Ködding, V. Lohweg, M. Malatyali, F. Meyer auf der Heide, M. Panzner, C. Soltenborn, in: Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21), 2021.","ieee":"R. Bernijazov et al., “A Meta-Review on Artificial Intelligence in Product Creation,” presented at the 30th International Joint Conference on Artificial Intelligence (IJCAI 2021) - Workshop “AI and Product Design,” Montreal, Kanada, 2021.","ama":"Bernijazov R, Dicks A, Dumitrescu R, et al. A Meta-Review on Artificial Intelligence in Product Creation. In: Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21). ; 2021.","apa":"Bernijazov, R., Dicks, A., Dumitrescu, R., Foullois, M., Hanselle, J. M., Hüllermeier, E., Karakaya, G., Ködding, P., Lohweg, V., Malatyali, M., Meyer auf der Heide, F., Panzner, M., & Soltenborn, C. (2021). A Meta-Review on Artificial Intelligence in Product Creation. Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21). 30th International Joint Conference on Artificial Intelligence (IJCAI 2021) - Workshop “AI and Product Design,” Montreal, Kanada.","chicago":"Bernijazov, Ruslan, Alexander Dicks, Roman Dumitrescu, Marc Foullois, Jonas Manuel Hanselle, Eyke Hüllermeier, Gökce Karakaya, et al. “A Meta-Review on Artificial Intelligence in Product Creation.” In Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21), 2021.","bibtex":"@inproceedings{Bernijazov_Dicks_Dumitrescu_Foullois_Hanselle_Hüllermeier_Karakaya_Ködding_Lohweg_Malatyali_et al._2021, title={A Meta-Review on Artificial Intelligence in Product Creation}, booktitle={Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21)}, author={Bernijazov, Ruslan and Dicks, Alexander and Dumitrescu, Roman and Foullois, Marc and Hanselle, Jonas Manuel and Hüllermeier, Eyke and Karakaya, Gökce and Ködding, Patrick and Lohweg, Volker and Malatyali, Manuel and et al.}, year={2021} }","mla":"Bernijazov, Ruslan, et al. “A Meta-Review on Artificial Intelligence in Product Creation.” Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21), 2021."},"language":[{"iso":"eng"}],"abstract":[{"lang":"ger","text":"Produktentstehung (PE) bezieht sich auf den Prozess der Planung und Entwicklung eines Produkts sowie der damit verbundenen Dienstleistungen von der ersten Idee bis zur Herstellung und zum Vertrieb. Während dieses Prozesses gibt es zahlreiche Aufgaben, die von menschlichem Fachwissen abhängen und typischerweise von erfahrenen Experten übernommen werden. Da sich das Feld der Künstlichen Intelligenz (KI) immer weiterentwickelt und seinen Weg in den Fertigungssektor findet, gibt es viele Möglichkeiten für eine Anwendung von KI, um bei der Lösung der oben genannten Aufgaben zu helfen. In diesem Paper geben wir einen umfassenden Überblick über den aktuellen Stand der Technik des Einsatzes von KI in der PE. \r\nIm Detail analysieren wir 40 bestehende Surveys zu KI in der PE und 94 Case Studies, um herauszufinden, welche Bereiche der PE von der aktuellen Forschung in diesem Bereich vorrangig adressiert werden, wie ausgereift die diskutierten KI-Methoden sind und inwieweit datenzentrierte Ansätze in der aktuellen Forschung genutzt werden."},{"lang":"eng","text":"Product Creation (PC) refers to the process of planning and developing a product as well as related services from the initial idea until manufacturing and distribution. Throughout this process, there are numerous tasks that depend on human expertise and are typically undertaken by experienced practitioners. As the field of Artificial Intelligence (AI) continues to evolve and finds its way into the manufacturing sector, there exist many possibilities for an application of AI in order to assist in solving aforementioned tasks. In this work, we provide a comprehensive overview of the current state of the art of the use of AI in PC. \r\nIn detail, we analyze 40 existing surveys on AI in PC and 94 case studies in order to find out which areas of PC are primarily addressed by current research in this field, how mature the discussed AI methods are, and to which extent data-centric approaches are utilized in current research."}],"title":"A Meta-Review on Artificial Intelligence in Product Creation","user_id":"15415","publication":"Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21)","keyword":["Artificial Intelligence Product Creation Literature Review"],"department":[{"_id":"63"},{"_id":"563"},{"_id":"355"},{"_id":"241"}],"author":[{"last_name":"Bernijazov","first_name":"Ruslan","full_name":"Bernijazov, Ruslan"},{"last_name":"Dicks","full_name":"Dicks, Alexander","first_name":"Alexander"},{"first_name":"Roman","full_name":"Dumitrescu, Roman","last_name":"Dumitrescu","id":"16190"},{"last_name":"Foullois","full_name":"Foullois, Marc","first_name":"Marc"},{"full_name":"Hanselle, Jonas Manuel","orcid":"0000-0002-1231-4985","first_name":"Jonas Manuel","id":"43980","last_name":"Hanselle"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","id":"48129","last_name":"Hüllermeier"},{"first_name":"Gökce","full_name":"Karakaya, Gökce","last_name":"Karakaya"},{"last_name":"Ködding","id":"45402","first_name":"Patrick","full_name":"Ködding, Patrick"},{"last_name":"Lohweg","full_name":"Lohweg, Volker","first_name":"Volker"},{"id":"41265","last_name":"Malatyali","full_name":"Malatyali, Manuel","first_name":"Manuel"},{"id":"15523","last_name":"Meyer auf der Heide","full_name":"Meyer auf der Heide, Friedhelm","first_name":"Friedhelm"},{"full_name":"Panzner, Melina","first_name":"Melina","last_name":"Panzner"},{"last_name":"Soltenborn","id":"1737","first_name":"Christian","full_name":"Soltenborn, Christian","orcid":"0000-0002-0342-8227"}],"quality_controlled":"1","publication_status":"epub_ahead","date_created":"2021-09-06T08:23:45Z","status":"public"},{"year":"2021","citation":{"short":"E. Hüllermeier, F. Mohr, A. Tornede, M.D. Wever, in: 2021.","ieee":"E. Hüllermeier, F. Mohr, A. Tornede, and M. D. Wever, “Automated Machine Learning, Bounded Rationality, and Rational Metareasoning,” presented at the ECML/PKDD Workshop on Automating Data Science, Bilbao (Virtual), 2021.","chicago":"Hüllermeier, Eyke, Felix Mohr, Alexander Tornede, and Marcel Dominik Wever. “Automated Machine Learning, Bounded Rationality, and Rational Metareasoning,” 2021.","ama":"Hüllermeier E, Mohr F, Tornede A, Wever MD. Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. In: ; 2021.","apa":"Hüllermeier, E., Mohr, F., Tornede, A., & Wever, M. D. (2021). Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. ECML/PKDD Workshop on Automating Data Science, Bilbao (Virtual).","bibtex":"@inproceedings{Hüllermeier_Mohr_Tornede_Wever_2021, title={Automated Machine Learning, Bounded Rationality, and Rational Metareasoning}, author={Hüllermeier, Eyke and Mohr, Felix and Tornede, Alexander and Wever, Marcel Dominik}, year={2021} }","mla":"Hüllermeier, Eyke, et al. Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. 2021."},"type":"conference","language":[{"iso":"eng"}],"_id":"22913","date_updated":"2022-01-06T06:55:43Z","conference":{"location":"Bilbao (Virtual)","name":"ECML/PKDD Workshop on Automating Data Science","start_date":"2021-09-13","end_date":"2021-09-17"},"status":"public","project":[{"name":"SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - Project Area B"},{"_id":"10","name":"SFB 901 - Subproject B2"}],"date_created":"2021-08-02T07:46:29Z","author":[{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","id":"48129","last_name":"Hüllermeier"},{"last_name":"Mohr","full_name":"Mohr, Felix","first_name":"Felix"},{"last_name":"Tornede","id":"38209","first_name":"Alexander","full_name":"Tornede, Alexander"},{"first_name":"Marcel Dominik","full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","id":"33176"}],"quality_controlled":"1","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"title":"Automated Machine Learning, Bounded Rationality, and Rational Metareasoning","user_id":"5786"},{"conference":{"end_date":"2021-07-23","location":"Virtual","name":"8th ICML Workshop on Automated Machine Learning","start_date":"2021-07-23"},"_id":"22914","date_updated":"2022-01-06T06:55:43Z","language":[{"iso":"eng"}],"citation":{"bibtex":"@inproceedings{Mohr_Wever_2021, title={Replacing the Ex-Def Baseline in AutoML by Naive AutoML}, author={Mohr, Felix and Wever, Marcel Dominik}, year={2021} }","mla":"Mohr, Felix, and Marcel Dominik Wever. Replacing the Ex-Def Baseline in AutoML by Naive AutoML. 2021.","apa":"Mohr, F., & Wever, M. D. (2021). Replacing the Ex-Def Baseline in AutoML by Naive AutoML. 8th ICML Workshop on Automated Machine Learning, Virtual.","ama":"Mohr F, Wever MD. Replacing the Ex-Def Baseline in AutoML by Naive AutoML. In: ; 2021.","chicago":"Mohr, Felix, and Marcel Dominik Wever. “Replacing the Ex-Def Baseline in AutoML by Naive AutoML,” 2021.","ieee":"F. Mohr and M. D. Wever, “Replacing the Ex-Def Baseline in AutoML by Naive AutoML,” presented at the 8th ICML Workshop on Automated Machine Learning, Virtual, 2021.","short":"F. Mohr, M.D. Wever, in: 2021."},"year":"2021","type":"conference","user_id":"5786","title":"Replacing the Ex-Def Baseline in AutoML by Naive AutoML","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"author":[{"last_name":"Mohr","first_name":"Felix","full_name":"Mohr, Felix"},{"last_name":"Wever","id":"33176","first_name":"Marcel Dominik","full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818"}],"date_created":"2021-08-02T07:48:07Z","status":"public"}]