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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 ","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."},"type":"dissertation","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"},{"name":"SFB 901 - B: SFB 901 - Project Area B","_id":"3"},{"_id":"1","grant_number":"160364472","name":"SFB 901: SFB 901: On-The-Fly Computing - Individualisierte IT-Dienstleistungen in dynamischen Märkten "}],"date_updated":"2023-08-04T06:01:49Z","oa":"1","doi":"10.17619/UNIPB/1-1780 ","language":[{"iso":"eng"}]},{"department":[{"_id":"101"},{"_id":"636"},{"_id":"355"},{"_id":"655"}],"publication_status":"published","external_id":{"arxiv":["arXiv:2104.03562"]},"title":"Efficient time stepping for numerical integration using reinforcement learning","related_material":{"link":[{"description":"GitHub","relation":"software","url":"https://github.com/lueckem/quadrature-ML"}]},"language":[{"iso":"eng"}],"date_updated":"2023-08-25T09:24:50Z","doi":"10.1137/21M1412682","author":[{"last_name":"Dellnitz","full_name":"Dellnitz, Michael","first_name":"Michael"},{"last_name":"Hüllermeier","id":"48129","first_name":"Eyke","full_name":"Hüllermeier, Eyke"},{"full_name":"Lücke, Marvin","first_name":"Marvin","last_name":"Lücke"},{"id":"16494","last_name":"Ober-Blöbaum","full_name":"Ober-Blöbaum, Sina","first_name":"Sina"},{"last_name":"Offen","id":"85279","first_name":"Christian","orcid":"0000-0002-5940-8057","full_name":"Offen, Christian"},{"orcid":"0000-0002-3389-793X","full_name":"Peitz, Sebastian","first_name":"Sebastian","id":"47427","last_name":"Peitz"},{"id":"13472","last_name":"Pfannschmidt","orcid":"0000-0001-9407-7903","full_name":"Pfannschmidt, Karlson","first_name":"Karlson"}],"publication":"SIAM Journal on Scientific Computing","volume":45,"has_accepted_license":"1","status":"public","date_created":"2021-04-09T07:59:19Z","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"}],"ddc":["510"],"user_id":"47427","main_file_link":[{"url":"https://epubs.siam.org/doi/reader/10.1137/21M1412682"}],"year":"2023","type":"journal_article","citation":{"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.","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.","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} }","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.","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.","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","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"},"page":"A579-A595","_id":"21600","intvolume":" 45","issue":"2"},{"author":[{"id":"83151","last_name":"Gevers","full_name":"Gevers, Karina","first_name":"Karina"},{"last_name":"Schöppner","id":"20530","first_name":"Volker","full_name":"Schöppner, Volker"},{"last_name":"Hüllermeier","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}],"department":[{"_id":"367"},{"_id":"355"},{"_id":"321"}],"status":"public","date_created":"2021-09-14T11:34:31Z","user_id":"83151","title":"Heated tool butt welding of two different materials – Established methods versus artificial intelligence","language":[{"iso":"eng"}],"type":"conference","year":"2021","citation":{"mla":"Gevers, Karina, et al. 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} }","chicago":"Gevers, Karina, Volker Schöppner, and Eyke Hüllermeier. “Heated Tool Butt Welding of Two Different Materials – Established Methods versus Artificial Intelligence,” 2021.","ama":"Gevers K, Schöppner V, Hüllermeier E. Heated tool butt welding of two different materials – Established methods versus artificial intelligence. In: ; 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.","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.","short":"K. Gevers, V. Schöppner, E. Hüllermeier, in: 2021."},"date_updated":"2022-01-06T06:56:19Z","_id":"24382","conference":{"end_date":"2021-07-14","name":"International Institute of Welding","start_date":"2021-07-12","location":"online"}},{"page":"1-1","citation":{"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.","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.","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","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.","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} }"},"year":"2021","type":"journal_article","language":[{"iso":"eng"}],"_id":"21004","date_updated":"2022-01-06T06:54:42Z","doi":"10.1109/tpami.2021.3051276","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"keyword":["Automated Machine Learning","Multi Label Classification","Hierarchical Planning","Bayesian Optimization"],"publication":"IEEE Transactions on Pattern Analysis and Machine Intelligence","author":[{"id":"33176","last_name":"Wever","full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik"},{"full_name":"Tornede, Alexander","first_name":"Alexander","id":"38209","last_name":"Tornede"},{"full_name":"Mohr, Felix","first_name":"Felix","last_name":"Mohr"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","id":"48129","last_name":"Hüllermeier"}],"publication_status":"published","publication_identifier":{"issn":["0162-8828","2160-9292","1939-3539"]},"project":[{"name":"SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - Project Area B"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"date_created":"2021-01-16T14:48:13Z","status":"public","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"}],"title":"AutoML for Multi-Label Classification: Overview and Empirical Evaluation","user_id":"5786"},{"language":[{"iso":"eng"}],"citation":{"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.” IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE.","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.","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.","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.","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.","short":"F. Mohr, M.D. Wever, A. Tornede, E. Hüllermeier, IEEE Transactions on Pattern Analysis and Machine Intelligence (n.d.)."},"year":"2021","type":"journal_article","_id":"21092","date_updated":"2022-01-06T06:54:45Z","author":[{"full_name":"Mohr, Felix","first_name":"Felix","last_name":"Mohr"},{"id":"33176","last_name":"Wever","full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik"},{"first_name":"Alexander","full_name":"Tornede, Alexander","last_name":"Tornede","id":"38209"},{"id":"48129","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"publisher":"IEEE","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"publication":"IEEE Transactions on Pattern Analysis and Machine Intelligence","status":"public","date_created":"2021-01-27T13:45:52Z","project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Project Area B","_id":"3"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"publication_status":"accepted","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"}],"user_id":"5786","title":"Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning"},{"issue":"7","intvolume":" 22","_id":"21535","date_updated":"2022-01-06T06:55:03Z","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.","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.","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} }","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.","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.","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."},"year":"2021","user_id":"76599","title":"Preference-based Online Learning with Dueling Bandits: A Survey","date_created":"2021-03-18T11:15:38Z","status":"public","volume":22,"publication":"Journal of Machine Learning Research","department":[{"_id":"34"},{"_id":"7"},{"_id":"355"}],"quality_controlled":"1","author":[{"last_name":"Bengs","first_name":"Viktor","full_name":"Bengs, Viktor"},{"first_name":"Róbert","full_name":"Busa-Fekete, Róbert","last_name":"Busa-Fekete"},{"last_name":"El Mesaoudi-Paul","full_name":"El Mesaoudi-Paul, Adil","first_name":"Adil"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier"}]},{"conference":{"name":"Genetic and Evolutionary Computation Conference","start_date":"2021-07-10","end_date":"2021-07-14"},"date_updated":"2022-01-06T06:55:06Z","_id":"21570","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.","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.","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.","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} }","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."},"language":[{"iso":"eng"}],"title":"Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance","user_id":"5786","project":[{"_id":"1","name":"SFB 901"},{"_id":"3","name":"SFB 901 - Project Area B"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"date_created":"2021-03-26T09:14:19Z","status":"public","publication":"Proceedings of the Genetic and Evolutionary Computation Conference","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"author":[{"full_name":"Tornede, Tanja","first_name":"Tanja","id":"40795","last_name":"Tornede"},{"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"},{"last_name":"Hüllermeier","id":"48129","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}]},{"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."}],"user_id":"15415","title":"A Meta-Review on Artificial Intelligence in Product Creation","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","full_name":"Bernijazov, Ruslan","first_name":"Ruslan"},{"full_name":"Dicks, Alexander","first_name":"Alexander","last_name":"Dicks"},{"first_name":"Roman","full_name":"Dumitrescu, Roman","last_name":"Dumitrescu","id":"16190"},{"first_name":"Marc","full_name":"Foullois, Marc","last_name":"Foullois"},{"last_name":"Hanselle","id":"43980","first_name":"Jonas Manuel","orcid":"0000-0002-1231-4985","full_name":"Hanselle, Jonas Manuel"},{"last_name":"Hüllermeier","id":"48129","first_name":"Eyke","full_name":"Hüllermeier, Eyke"},{"full_name":"Karakaya, Gökce","first_name":"Gökce","last_name":"Karakaya"},{"first_name":"Patrick","full_name":"Ködding, Patrick","last_name":"Ködding","id":"45402"},{"last_name":"Lohweg","first_name":"Volker","full_name":"Lohweg, Volker"},{"last_name":"Malatyali","id":"41265","first_name":"Manuel","full_name":"Malatyali, Manuel"},{"id":"15523","last_name":"Meyer auf der Heide","full_name":"Meyer auf der Heide, Friedhelm","first_name":"Friedhelm"},{"first_name":"Melina","full_name":"Panzner, Melina","last_name":"Panzner"},{"id":"1737","last_name":"Soltenborn","orcid":"0000-0002-0342-8227","full_name":"Soltenborn, Christian","first_name":"Christian"}],"quality_controlled":"1","date_created":"2021-09-06T08:23:45Z","status":"public","publication_status":"epub_ahead","conference":{"start_date":"2021-08-19","name":"30th International Joint Conference on Artificial Intelligence (IJCAI 2021) - Workshop \"AI and Product Design\"","location":"Montreal, Kanada","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"}],"language":[{"iso":"eng"}],"type":"conference","citation":{"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.","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.","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.","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.","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.","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."},"year":"2021"},{"status":"public","project":[{"_id":"1","name":"SFB 901"},{"_id":"3","name":"SFB 901 - Project Area B"},{"_id":"10","name":"SFB 901 - Subproject B2"}],"date_created":"2021-08-02T07:46:29Z","quality_controlled":"1","author":[{"last_name":"Hüllermeier","id":"48129","first_name":"Eyke","full_name":"Hüllermeier, Eyke"},{"first_name":"Felix","full_name":"Mohr, Felix","last_name":"Mohr"},{"id":"38209","last_name":"Tornede","full_name":"Tornede, Alexander","first_name":"Alexander"},{"orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik","first_name":"Marcel Dominik","id":"33176","last_name":"Wever"}],"department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"user_id":"5786","title":"Automated Machine Learning, Bounded Rationality, and Rational Metareasoning","language":[{"iso":"eng"}],"year":"2021","citation":{"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.","short":"E. Hüllermeier, F. Mohr, A. Tornede, M.D. Wever, in: 2021.","mla":"Hüllermeier, Eyke, et al. Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. 2021.","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} }","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)."},"type":"conference","date_updated":"2022-01-06T06:55:43Z","_id":"22913","conference":{"end_date":"2021-09-17","name":"ECML/PKDD Workshop on Automating Data Science","start_date":"2021-09-13","location":"Bilbao (Virtual)"}},{"date_created":"2021-08-02T07:48:07Z","status":"public","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"author":[{"full_name":"Mohr, Felix","first_name":"Felix","last_name":"Mohr"},{"first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik","last_name":"Wever","id":"33176"}],"user_id":"5786","title":"Replacing the Ex-Def Baseline in AutoML by Naive AutoML","language":[{"iso":"eng"}],"type":"conference","citation":{"short":"F. Mohr, M.D. Wever, in: 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.","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.","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."},"year":"2021","conference":{"location":"Virtual","start_date":"2021-07-23","name":"8th ICML Workshop on Automated Machine Learning","end_date":"2021-07-23"},"_id":"22914","date_updated":"2022-01-06T06:55:43Z"},{"title":"Ranking Structured Objects with Graph Neural Networks","external_id":{"arxiv":["2104.08869"]},"publication_status":"published","publication_identifier":{"issn":["0302-9743","1611-3349"],"isbn":["9783030889418","9783030889425"]},"editor":[{"first_name":"Carlos","full_name":"Soares, Carlos","last_name":"Soares"},{"last_name":"Torgo","full_name":"Torgo, Luis","first_name":"Luis"}],"department":[{"_id":"355"}],"doi":"10.1007/978-3-030-88942-5","date_updated":"2022-04-11T22:08:12Z","language":[{"iso":"eng"}],"series_title":"Lecture Notes in Computer Science","user_id":"48192","abstract":[{"lang":"eng","text":"Graph neural networks (GNNs) have been successfully applied in many structured data domains, with applications ranging from molecular property prediction to the analysis of social networks. Motivated by the broad applicability of GNNs, we propose the family of so-called RankGNNs, a combination of neural Learning to Rank (LtR) methods and GNNs. RankGNNs are trained with a set of pair-wise preferences between graphs, suggesting that one of them is preferred over the other. One practical application of this problem is drug screening, where an expert wants to find the most promising molecules in a large collection of drug candidates. We empirically demonstrate that our proposed pair-wise RankGNN approach either significantly outperforms or at least matches the ranking performance of the naive point-wise baseline approach, in which the LtR problem is solved via GNN-based graph regression."}],"volume":12986,"date_created":"2021-11-11T14:15:18Z","status":"public","publication":"Proceedings of The 24th International Conference on Discovery Science (DS 2021)","keyword":["Graph-structured data","Graph neural networks","Preference learning","Learning to rank"],"publisher":"Springer","author":[{"last_name":"Damke","id":"48192","first_name":"Clemens","full_name":"Damke, Clemens","orcid":"0000-0002-0455-0048"},{"id":"48129","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"quality_controlled":"1","conference":{"end_date":"2021-10-13","start_date":"2021-10-11","name":"24th International Conference on Discovery Science","location":"Halifax, Canada"},"intvolume":" 12986","_id":"27381","page":"166-180","citation":{"short":"C. Damke, E. Hüllermeier, in: C. Soares, L. Torgo (Eds.), Proceedings of The 24th International Conference on Discovery Science (DS 2021), Springer, 2021, pp. 166–180.","ieee":"C. Damke and E. Hüllermeier, “Ranking Structured Objects with Graph Neural Networks,” in Proceedings of The 24th International Conference on Discovery Science (DS 2021), Halifax, Canada, 2021, vol. 12986, pp. 166–180, doi: 10.1007/978-3-030-88942-5.","apa":"Damke, C., & Hüllermeier, E. (2021). Ranking Structured Objects with Graph Neural Networks. In C. Soares & L. Torgo (Eds.), Proceedings of The 24th International Conference on Discovery Science (DS 2021) (Vol. 12986, pp. 166–180). Springer. https://doi.org/10.1007/978-3-030-88942-5","ama":"Damke C, Hüllermeier E. Ranking Structured Objects with Graph Neural Networks. In: Soares C, Torgo L, eds. Proceedings of The 24th International Conference on Discovery Science (DS 2021). Vol 12986. Lecture Notes in Computer Science. Springer; 2021:166-180. doi:10.1007/978-3-030-88942-5","chicago":"Damke, Clemens, and Eyke Hüllermeier. “Ranking Structured Objects with Graph Neural Networks.” In Proceedings of The 24th International Conference on Discovery Science (DS 2021), edited by Carlos Soares and Luis Torgo, 12986:166–80. Lecture Notes in Computer Science. 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Wever, Automated Machine Learning for Multi-Label Classification. 2021.","short":"M.D. Wever, Automated Machine Learning for Multi-Label Classification, 2021.","mla":"Wever, Marcel Dominik. Automated Machine Learning for Multi-Label Classification. 2021, doi:10.17619/UNIPB/1-1302.","bibtex":"@book{Wever_2021, title={Automated Machine Learning for Multi-Label Classification}, DOI={10.17619/UNIPB/1-1302}, author={Wever, Marcel Dominik}, year={2021} }","apa":"Wever, M. D. (2021). Automated Machine Learning for Multi-Label Classification. https://doi.org/10.17619/UNIPB/1-1302","ama":"Wever MD. Automated Machine Learning for Multi-Label Classification.; 2021. doi:10.17619/UNIPB/1-1302","chicago":"Wever, Marcel Dominik. Automated Machine Learning for Multi-Label Classification, 2021. https://doi.org/10.17619/UNIPB/1-1302."},"supervisor":[{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","id":"48129","last_name":"Hüllermeier"}],"ddc":["000"],"user_id":"33176","file_date_updated":"2022-04-13T09:39:56Z","author":[{"id":"33176","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik","first_name":"Marcel Dominik"}],"file":[{"date_created":"2022-04-13T09:35:25Z","file_name":"dissertation_publish_upload.pdf","access_level":"open_access","file_id":"30886","creator":"wever","file_size":8098177,"relation":"main_file","date_updated":"2022-04-13T09:39:56Z","content_type":"application/pdf"}],"date_created":"2021-11-08T14:05:19Z","has_accepted_license":"1","status":"public","date_updated":"2022-04-13T09:39:56Z","doi":"10.17619/UNIPB/1-1302","oa":"1","language":[{"iso":"eng"}],"title":"Automated Machine Learning for Multi-Label Classification","department":[{"_id":"355"}],"publication_status":"published","project":[{"name":"SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - Project Area B"},{"name":"SFB 901 - Subproject B2","_id":"10"}]},{"series_title":"PAKDD","language":[{"iso":"eng"}],"year":"2021","type":"conference","citation":{"ieee":"J. M. Hanselle, A. Tornede, M. D. Wever, and E. Hüllermeier, “Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data.” 2021.","short":"J.M. Hanselle, A. Tornede, M.D. Wever, E. Hüllermeier, (2021).","mla":"Hanselle, Jonas Manuel, et al. Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data. 2021.","bibtex":"@article{Hanselle_Tornede_Wever_Hüllermeier_2021, series={PAKDD}, title={Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data}, author={Hanselle, Jonas Manuel and Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2021}, collection={PAKDD} }","ama":"Hanselle JM, Tornede A, Wever MD, Hüllermeier E. Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data. Published online 2021.","apa":"Hanselle, J. M., Tornede, A., Wever, M. D., & Hüllermeier, E. (2021). Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data. The 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2021), Delhi, India.","chicago":"Hanselle, Jonas Manuel, Alexander Tornede, Marcel Dominik Wever, and Eyke Hüllermeier. “Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data.” PAKDD, 2021."},"_id":"21198","date_updated":"2022-08-24T12:49:06Z","conference":{"location":"Delhi, India","name":"The 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2021)","start_date":"2021-05-11","end_date":"2021-05-14"},"author":[{"first_name":"Jonas Manuel","orcid":"0000-0002-1231-4985","full_name":"Hanselle, Jonas Manuel","last_name":"Hanselle","id":"43980"},{"id":"38209","last_name":"Tornede","full_name":"Tornede, Alexander","first_name":"Alexander"},{"first_name":"Marcel Dominik","full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","id":"33176"},{"id":"48129","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"status":"public","date_created":"2021-02-09T09:30:14Z","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"}],"user_id":"38209","title":"Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data"},{"doi":"10.1007/978-3-030-58285-2_30","date_updated":"2022-01-06T06:54:06Z","_id":"19521","language":[{"iso":"eng"}],"year":"2020","type":"book_chapter","citation":{"bibtex":"@inbook{Pfannschmidt_Hüllermeier_2020, place={Cham}, title={Learning Choice Functions via Pareto-Embeddings}, DOI={10.1007/978-3-030-58285-2_30}, booktitle={Lecture Notes in Computer Science}, author={Pfannschmidt, Karlson and Hüllermeier, Eyke}, year={2020} }","mla":"Pfannschmidt, Karlson, and Eyke Hüllermeier. “Learning Choice Functions via Pareto-Embeddings.” Lecture Notes in Computer Science, 2020, doi:10.1007/978-3-030-58285-2_30.","chicago":"Pfannschmidt, Karlson, and Eyke Hüllermeier. “Learning Choice Functions via Pareto-Embeddings.” In Lecture Notes in Computer Science. Cham, 2020. https://doi.org/10.1007/978-3-030-58285-2_30.","ama":"Pfannschmidt K, Hüllermeier E. Learning Choice Functions via Pareto-Embeddings. In: Lecture Notes in Computer Science. Cham; 2020. doi:10.1007/978-3-030-58285-2_30","apa":"Pfannschmidt, K., & Hüllermeier, E. (2020). Learning Choice Functions via Pareto-Embeddings. In Lecture Notes in Computer Science. Cham. https://doi.org/10.1007/978-3-030-58285-2_30","ieee":"K. Pfannschmidt and E. Hüllermeier, “Learning Choice Functions via Pareto-Embeddings,” in Lecture Notes in Computer Science, Cham, 2020.","short":"K. Pfannschmidt, E. Hüllermeier, in: Lecture Notes in Computer Science, Cham, 2020."},"user_id":"13472","title":"Learning Choice Functions via Pareto-Embeddings","place":"Cham","project":[{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"date_created":"2020-09-17T10:52:41Z","status":"public","publication_status":"published","publication_identifier":{"issn":["0302-9743","1611-3349"],"isbn":["9783030582845","9783030582852"]},"publication":"Lecture Notes in Computer Science","department":[{"_id":"7"},{"_id":"355"}],"author":[{"first_name":"Karlson","full_name":"Pfannschmidt, Karlson","last_name":"Pfannschmidt"},{"last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}]},{"year":"2020","citation":{"ieee":"C. Damke, V. Melnikov, and E. Hüllermeier, “A Novel Higher-order Weisfeiler-Lehman Graph Convolution,” in Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020), Bangkok, Thailand, 2020, vol. 129, pp. 49–64.","short":"C. Damke, V. Melnikov, E. Hüllermeier, in: S. Jialin Pan, M. Sugiyama (Eds.), Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020), PMLR, Bangkok, Thailand, 2020, pp. 49–64.","bibtex":"@inproceedings{Damke_Melnikov_Hüllermeier_2020, place={Bangkok, Thailand}, series={Proceedings of Machine Learning Research}, title={A Novel Higher-order Weisfeiler-Lehman Graph Convolution}, volume={129}, booktitle={Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020)}, publisher={PMLR}, author={Damke, Clemens and Melnikov, Vitaly and Hüllermeier, Eyke}, editor={Jialin Pan, Sinno and Sugiyama, MasashiEditors}, year={2020}, pages={49–64}, collection={Proceedings of Machine Learning Research} }","mla":"Damke, Clemens, et al. “A Novel Higher-Order Weisfeiler-Lehman Graph Convolution.” Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020), edited by Sinno Jialin Pan and Masashi Sugiyama, vol. 129, PMLR, 2020, pp. 49–64.","apa":"Damke, C., Melnikov, V., & Hüllermeier, E. (2020). A Novel Higher-order Weisfeiler-Lehman Graph Convolution. In S. Jialin Pan & M. Sugiyama (Eds.), Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020) (Vol. 129, pp. 49–64). Bangkok, Thailand: PMLR.","ama":"Damke C, Melnikov V, Hüllermeier E. A Novel Higher-order Weisfeiler-Lehman Graph Convolution. In: Jialin Pan S, Sugiyama M, eds. Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020). Vol 129. Proceedings of Machine Learning Research. Bangkok, Thailand: PMLR; 2020:49-64.","chicago":"Damke, Clemens, Vitaly Melnikov, and Eyke Hüllermeier. “A Novel Higher-Order Weisfeiler-Lehman Graph Convolution.” In Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020), edited by Sinno Jialin Pan and Masashi Sugiyama, 129:49–64. Proceedings of Machine Learning Research. Bangkok, Thailand: PMLR, 2020."},"type":"conference","page":"49-64","intvolume":" 129","_id":"19953","conference":{"start_date":"2020-11-18","name":"Asian Conference on Machine Learning","location":"Bangkok, Thailand","end_date":"2020-11-20"},"quality_controlled":"1","publisher":"PMLR","author":[{"id":"48192","last_name":"Damke","orcid":"0000-0002-0455-0048","full_name":"Damke, Clemens","first_name":"Clemens"},{"first_name":"Vitaly","full_name":"Melnikov, Vitaly","last_name":"Melnikov","id":"58747"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","id":"48129"}],"file_date_updated":"2020-10-08T11:24:29Z","keyword":["graph neural networks","Weisfeiler-Lehman test","cycle detection"],"publication":"Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020)","file":[{"file_name":"damke20.pdf","date_created":"2020-10-08T10:54:48Z","access_level":"open_access","file_size":771137,"file_id":"19954","creator":"cdamke","content_type":"application/pdf","date_updated":"2020-10-08T11:21:00Z","relation":"main_file"},{"file_size":613163,"file_id":"19955","creator":"cdamke","date_updated":"2020-10-08T11:24:29Z","content_type":"application/pdf","relation":"supplementary_material","date_created":"2020-10-08T10:54:59Z","file_name":"damke20-supp.pdf","access_level":"open_access"}],"volume":129,"has_accepted_license":"1","status":"public","date_created":"2020-10-08T10:48:38Z","abstract":[{"lang":"eng","text":"Current GNN architectures use a vertex neighborhood aggregation scheme, which limits their discriminative power to that of the 1-dimensional Weisfeiler-Lehman (WL) graph isomorphism test. Here, we propose a novel graph convolution operator that is based on the 2-dimensional WL test. We formally show that the resulting 2-WL-GNN architecture is more discriminative than existing GNN approaches. This theoretical result is complemented by experimental studies using synthetic and real data. On multiple common graph classification benchmarks, we demonstrate that the proposed model is competitive with state-of-the-art graph kernels and GNNs."}],"ddc":["006"],"user_id":"48192","series_title":"Proceedings of Machine Learning Research","language":[{"iso":"eng"}],"date_updated":"2022-01-06T06:54:17Z","oa":"1","department":[{"_id":"355"}],"editor":[{"last_name":"Jialin Pan","first_name":"Sinno","full_name":"Jialin Pan, Sinno"},{"last_name":"Sugiyama","first_name":"Masashi","full_name":"Sugiyama, Masashi"}],"publication_status":"published","place":"Bangkok, Thailand","external_id":{"arxiv":["2007.00346"]},"title":"A Novel Higher-order Weisfeiler-Lehman Graph Convolution"},{"date_created":"2021-03-18T11:13:12Z","project":[{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"status":"public","department":[{"_id":"34"},{"_id":"7"},{"_id":"355"}],"publication":"International Conference on Machine Learning","author":[{"full_name":"Bengs, Viktor","first_name":"Viktor","last_name":"Bengs"},{"last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"user_id":"76599","title":"Preselection Bandits","language":[{"iso":"eng"}],"page":"778-787","type":"conference","citation":{"apa":"Bengs, V., & Hüllermeier, E. (2020). Preselection Bandits. In International Conference on Machine Learning (pp. 778–787).","ama":"Bengs V, Hüllermeier E. Preselection Bandits. In: International Conference on Machine Learning. ; 2020:778-787.","chicago":"Bengs, Viktor, and Eyke Hüllermeier. “Preselection Bandits.” In International Conference on Machine Learning, 778–87, 2020.","bibtex":"@inproceedings{Bengs_Hüllermeier_2020, title={Preselection Bandits}, booktitle={International Conference on Machine Learning}, author={Bengs, Viktor and Hüllermeier, Eyke}, year={2020}, pages={778–787} }","mla":"Bengs, Viktor, and Eyke Hüllermeier. “Preselection Bandits.” International Conference on Machine Learning, 2020, pp. 778–87.","short":"V. Bengs, E. Hüllermeier, in: International Conference on Machine Learning, 2020, pp. 778–787.","ieee":"V. Bengs and E. Hüllermeier, “Preselection Bandits,” in International Conference on Machine Learning, 2020, pp. 778–787."},"year":"2020","_id":"21534","date_updated":"2022-01-06T06:55:03Z"},{"abstract":[{"lang":"eng","text":"We consider a resource-aware variant of the classical multi-armed bandit\r\nproblem: In each round, the learner selects an arm and determines a resource\r\nlimit. It then observes a corresponding (random) reward, provided the (random)\r\namount of consumed resources remains below the limit. Otherwise, the\r\nobservation is censored, i.e., no reward is obtained. For this problem setting,\r\nwe introduce a measure of regret, which incorporates the actual amount of\r\nallocated resources of each learning round as well as the optimality of\r\nrealizable rewards. Thus, to minimize regret, the learner needs to set a\r\nresource limit and choose an arm in such a way that the chance to realize a\r\nhigh reward within the predefined resource limit is high, while the resource\r\nlimit itself should be kept as low as possible. We derive the theoretical lower\r\nbound on the cumulative regret and propose a learning algorithm having a regret\r\nupper bound that matches the lower bound. In a simulation study, we show that\r\nour learning algorithm outperforms straightforward extensions of standard\r\nmulti-armed bandit algorithms."}],"title":"Multi-Armed Bandits with Censored Consumption of Resources","user_id":"76599","department":[{"_id":"34"},{"_id":"7"},{"_id":"355"}],"publication":"arXiv:2011.00813","author":[{"last_name":"Bengs","full_name":"Bengs, Viktor","first_name":"Viktor"},{"last_name":"Hüllermeier","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}],"project":[{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"date_created":"2021-03-18T11:27:37Z","status":"public","_id":"21536","date_updated":"2022-01-06T06:55:03Z","type":"preprint","citation":{"short":"V. Bengs, E. Hüllermeier, ArXiv:2011.00813 (2020).","ieee":"V. Bengs and E. Hüllermeier, “Multi-Armed Bandits with Censored Consumption of Resources,” arXiv:2011.00813. 2020.","chicago":"Bengs, Viktor, and Eyke Hüllermeier. “Multi-Armed Bandits with Censored Consumption of Resources.” ArXiv:2011.00813, 2020.","ama":"Bengs V, Hüllermeier E. Multi-Armed Bandits with Censored Consumption of Resources. arXiv:201100813. 2020.","apa":"Bengs, V., & Hüllermeier, E. (2020). Multi-Armed Bandits with Censored Consumption of Resources. ArXiv:2011.00813.","mla":"Bengs, Viktor, and Eyke Hüllermeier. “Multi-Armed Bandits with Censored Consumption of Resources.” ArXiv:2011.00813, 2020.","bibtex":"@article{Bengs_Hüllermeier_2020, title={Multi-Armed Bandits with Censored Consumption of Resources}, journal={arXiv:2011.00813}, author={Bengs, Viktor and Hüllermeier, Eyke}, year={2020} }"},"year":"2020","language":[{"iso":"eng"}]},{"publication":"Discovery Science","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"author":[{"last_name":"Tornede","id":"38209","first_name":"Alexander","full_name":"Tornede, Alexander"},{"last_name":"Wever","id":"33176","first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","id":"48129"}],"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":"2020-07-21T10:06:51Z","status":"public","title":"Extreme Algorithm Selection with Dyadic Feature Representation","user_id":"5786","year":"2020","citation":{"short":"A. Tornede, M.D. Wever, E. Hüllermeier, in: Discovery Science, 2020.","ieee":"A. Tornede, M. D. Wever, and E. Hüllermeier, “Extreme Algorithm Selection with Dyadic Feature Representation,” presented at the Discovery Science 2020, 2020.","chicago":"Tornede, Alexander, Marcel Dominik Wever, and Eyke Hüllermeier. “Extreme Algorithm Selection with Dyadic Feature Representation.” In Discovery Science, 2020.","ama":"Tornede A, Wever MD, Hüllermeier E. Extreme Algorithm Selection with Dyadic Feature Representation. In: Discovery Science. ; 2020.","apa":"Tornede, A., Wever, M. D., & Hüllermeier, E. (2020). Extreme Algorithm Selection with Dyadic Feature Representation. Discovery Science. Discovery Science 2020.","bibtex":"@inproceedings{Tornede_Wever_Hüllermeier_2020, title={Extreme Algorithm Selection with Dyadic Feature Representation}, booktitle={Discovery Science}, author={Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2020} }","mla":"Tornede, Alexander, et al. “Extreme Algorithm Selection with Dyadic Feature Representation.” Discovery Science, 2020."},"type":"conference","language":[{"iso":"eng"}],"conference":{"name":"Discovery Science 2020"},"date_updated":"2022-01-06T06:53:10Z","_id":"17407"},{"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":"2020-07-21T10:21:09Z","status":"public","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"publication":"KI 2020: Advances in Artificial Intelligence","author":[{"id":"43980","last_name":"Hanselle","full_name":"Hanselle, Jonas Manuel","orcid":"0000-0002-1231-4985","first_name":"Jonas Manuel"},{"id":"38209","last_name":"Tornede","full_name":"Tornede, Alexander","first_name":"Alexander"},{"orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik","first_name":"Marcel Dominik","id":"33176","last_name":"Wever"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","id":"48129","last_name":"Hüllermeier"}],"title":"Hybrid Ranking and Regression for Algorithm Selection","user_id":"5786","type":"conference","citation":{"ieee":"J. M. Hanselle, A. Tornede, M. D. Wever, and E. Hüllermeier, “Hybrid Ranking and Regression for Algorithm Selection,” presented at the 43rd German Conference on Artificial Intelligence, 2020.","short":"J.M. Hanselle, A. Tornede, M.D. Wever, E. Hüllermeier, in: KI 2020: Advances in Artificial Intelligence, 2020.","mla":"Hanselle, Jonas Manuel, et al. “Hybrid Ranking and Regression for Algorithm Selection.” KI 2020: Advances in Artificial Intelligence, 2020.","bibtex":"@inproceedings{Hanselle_Tornede_Wever_Hüllermeier_2020, title={Hybrid Ranking and Regression for Algorithm Selection}, booktitle={KI 2020: Advances in Artificial Intelligence}, author={Hanselle, Jonas Manuel and Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2020} }","apa":"Hanselle, J. M., Tornede, A., Wever, M. D., & Hüllermeier, E. (2020). Hybrid Ranking and Regression for Algorithm Selection. KI 2020: Advances in Artificial Intelligence. 43rd German Conference on Artificial Intelligence.","ama":"Hanselle JM, Tornede A, Wever MD, Hüllermeier E. Hybrid Ranking and Regression for Algorithm Selection. In: KI 2020: Advances in Artificial Intelligence. ; 2020.","chicago":"Hanselle, Jonas Manuel, Alexander Tornede, Marcel Dominik Wever, and Eyke Hüllermeier. “Hybrid Ranking and Regression for Algorithm Selection.” In KI 2020: Advances in Artificial Intelligence, 2020."},"year":"2020","language":[{"iso":"eng"}],"conference":{"name":"43rd German Conference on Artificial Intelligence"},"date_updated":"2022-01-06T06:53:10Z","_id":"17408"},{"project":[{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"},{"name":"SFB 901 - Project Area B","_id":"3"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"_id":"1","name":"SFB 901"},{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"date_created":"2020-07-28T09:17:41Z","status":"public","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"publication":"Proceedings of the ECMLPKDD 2020","author":[{"full_name":"Tornede, Tanja","first_name":"Tanja","id":"40795","last_name":"Tornede"},{"last_name":"Tornede","id":"38209","first_name":"Alexander","full_name":"Tornede, Alexander"},{"orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik","first_name":"Marcel Dominik","id":"33176","last_name":"Wever"},{"last_name":"Mohr","full_name":"Mohr, Felix","first_name":"Felix"},{"last_name":"Hüllermeier","id":"48129","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}],"title":"AutoML for Predictive Maintenance: One Tool to RUL Them All","user_id":"5786","year":"2020","type":"conference","citation":{"ieee":"T. Tornede, A. Tornede, M. D. Wever, F. Mohr, and E. Hüllermeier, “AutoML for Predictive Maintenance: One Tool to RUL Them All,” presented at the IOTStream Workshop @ ECMLPKDD 2020, 2020, doi: 10.1007/978-3-030-66770-2_8.","short":"T. Tornede, A. Tornede, M.D. Wever, F. Mohr, E. Hüllermeier, in: Proceedings of the ECMLPKDD 2020, 2020.","bibtex":"@inproceedings{Tornede_Tornede_Wever_Mohr_Hüllermeier_2020, title={AutoML for Predictive Maintenance: One Tool to RUL Them All}, DOI={10.1007/978-3-030-66770-2_8}, booktitle={Proceedings of the ECMLPKDD 2020}, author={Tornede, Tanja and Tornede, Alexander and Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2020} }","mla":"Tornede, Tanja, et al. “AutoML for Predictive Maintenance: One Tool to RUL Them All.” Proceedings of the ECMLPKDD 2020, 2020, doi:10.1007/978-3-030-66770-2_8.","ama":"Tornede T, Tornede A, Wever MD, Mohr F, Hüllermeier E. AutoML for Predictive Maintenance: One Tool to RUL Them All. In: Proceedings of the ECMLPKDD 2020. ; 2020. doi:10.1007/978-3-030-66770-2_8","apa":"Tornede, T., Tornede, A., Wever, M. D., Mohr, F., & Hüllermeier, E. (2020). AutoML for Predictive Maintenance: One Tool to RUL Them All. Proceedings of the ECMLPKDD 2020. IOTStream Workshop @ ECMLPKDD 2020. https://doi.org/10.1007/978-3-030-66770-2_8","chicago":"Tornede, Tanja, Alexander Tornede, Marcel Dominik Wever, Felix Mohr, and Eyke Hüllermeier. “AutoML for Predictive Maintenance: One Tool to RUL Them All.” In Proceedings of the ECMLPKDD 2020, 2020. https://doi.org/10.1007/978-3-030-66770-2_8."},"language":[{"iso":"eng"}],"doi":"10.1007/978-3-030-66770-2_8","conference":{"name":"IOTStream Workshop @ ECMLPKDD 2020"},"date_updated":"2022-01-06T06:53:11Z","_id":"17424"},{"user_id":"5786","title":"Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction","abstract":[{"text":"Syntactic annotation of corpora in the form of part-of-speech (POS) tags is a key requirement for both linguistic research and subsequent automated natural language processing (NLP) tasks. This problem is commonly tackled using machine learning methods, i.e., by training a POS tagger on a sufficiently large corpus of labeled data. \r\nWhile the problem of POS tagging can essentially be considered as solved for modern languages, historical corpora turn out to be much more difficult, especially due to the lack of native speakers and sparsity of training data. Moreover, most texts have no sentences as we know them today, nor a common orthography.\r\nThese irregularities render the task of automated POS tagging more difficult and error-prone. Under these circumstances, instead of forcing the POS tagger to predict and commit to a single tag, it should be enabled to express its uncertainty. In this paper, we consider POS tagging within the framework of set-valued prediction, which allows the POS tagger to express its uncertainty via predicting a set of candidate POS tags instead of guessing a single one. The goal is to guarantee a high confidence that the correct POS tag is included while keeping the number of candidates small.\r\nIn our experimental study, we find that extending state-of-the-art POS taggers to set-valued prediction yields more precise and robust taggings, especially for unknown words, i.e., words not occurring in the training data.","lang":"eng"}],"status":"public","project":[{"_id":"39","name":"InterGramm"}],"date_created":"2020-08-05T06:52:53Z","publication_status":"submitted","author":[{"full_name":"Heid, Stefan Helmut","orcid":"0000-0002-9461-7372","first_name":"Stefan Helmut","id":"39640","last_name":"Heid"},{"last_name":"Wever","id":"33176","first_name":"Marcel Dominik","full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818"},{"last_name":"Hüllermeier","id":"48129","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}],"publisher":"episciences","publication":"Journal of Data Mining and Digital Humanities","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"oa":"1","_id":"17605","date_updated":"2022-01-06T06:53:15Z","language":[{"iso":"eng"}],"citation":{"short":"S.H. Heid, M.D. Wever, E. Hüllermeier, Journal of Data Mining and Digital Humanities (n.d.).","ieee":"S. H. Heid, M. D. Wever, and E. Hüllermeier, “Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction,” Journal of Data Mining and Digital Humanities. episciences.","chicago":"Heid, Stefan Helmut, Marcel Dominik Wever, and Eyke Hüllermeier. “Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction.” Journal of Data Mining and Digital Humanities. episciences, n.d.","ama":"Heid SH, Wever MD, Hüllermeier E. Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction. Journal of Data Mining and Digital Humanities.","apa":"Heid, S. H., Wever, M. D., & Hüllermeier, E. (n.d.). Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction. In Journal of Data Mining and Digital Humanities. episciences.","mla":"Heid, Stefan Helmut, et al. “Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction.” Journal of Data Mining and Digital Humanities, episciences.","bibtex":"@article{Heid_Wever_Hüllermeier, title={Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction}, journal={Journal of Data Mining and Digital Humanities}, publisher={episciences}, author={Heid, Stefan Helmut and Wever, Marcel Dominik and Hüllermeier, Eyke} }"},"type":"preprint","year":"2020","main_file_link":[{"url":"https://arxiv.org/abs/2008.01377","open_access":"1"}]},{"department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"publication":"Workshop MetaLearn 2020 @ NeurIPS 2020","author":[{"first_name":"Alexander","full_name":"Tornede, Alexander","last_name":"Tornede","id":"38209"},{"last_name":"Wever","id":"33176","first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","id":"48129"}],"project":[{"_id":"1","name":"SFB 901"},{"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"}],"date_created":"2020-11-06T09:42:27Z","status":"public","title":"Towards Meta-Algorithm Selection","user_id":"5786","citation":{"chicago":"Tornede, Alexander, Marcel Dominik Wever, and Eyke Hüllermeier. “Towards Meta-Algorithm Selection.” In Workshop MetaLearn 2020 @ NeurIPS 2020, 2020.","apa":"Tornede, A., Wever, M. D., & Hüllermeier, E. (2020). Towards Meta-Algorithm Selection. Workshop MetaLearn 2020 @ NeurIPS 2020. Workshop MetaLearn 2020 @ NeurIPS 2020, Online.","ama":"Tornede A, Wever MD, Hüllermeier E. Towards Meta-Algorithm Selection. In: Workshop MetaLearn 2020 @ NeurIPS 2020. ; 2020.","bibtex":"@inproceedings{Tornede_Wever_Hüllermeier_2020, title={Towards Meta-Algorithm Selection}, booktitle={Workshop MetaLearn 2020 @ NeurIPS 2020}, author={Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2020} }","mla":"Tornede, Alexander, et al. “Towards Meta-Algorithm Selection.” Workshop MetaLearn 2020 @ NeurIPS 2020, 2020.","short":"A. Tornede, M.D. Wever, E. Hüllermeier, in: Workshop MetaLearn 2020 @ NeurIPS 2020, 2020.","ieee":"A. Tornede, M. D. Wever, and E. Hüllermeier, “Towards Meta-Algorithm Selection,” presented at the Workshop MetaLearn 2020 @ NeurIPS 2020, Online, 2020."},"type":"conference","year":"2020","language":[{"iso":"eng"}],"conference":{"location":"Online","name":"Workshop MetaLearn 2020 @ NeurIPS 2020"},"date_updated":"2022-01-06T06:54:26Z","_id":"20306"},{"date_updated":"2022-01-06T06:53:25Z","doi":"10.1007/978-3-030-53552-0_22","series_title":"Lecture Notes in Computer Science","language":[{"iso":"eng"}],"place":"Cham","title":"Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach","department":[{"_id":"34"},{"_id":"7"},{"_id":"355"}],"project":[{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"publication_identifier":{"isbn":["9783030535513","9783030535520"],"issn":["0302-9743","1611-3349"]},"publication_status":"published","intvolume":" 12096","_id":"18014","page":"216 - 232","year":"2020","citation":{"chicago":"El Mesaoudi-Paul, Adil, Dimitri Weiß, Viktor Bengs, Eyke Hüllermeier, and Kevin Tierney. “Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach.” In Learning and Intelligent Optimization. LION 2020., 12096:216–32. Lecture Notes in Computer Science. Cham: Springer, 2020. https://doi.org/10.1007/978-3-030-53552-0_22.","apa":"El Mesaoudi-Paul, A., Weiß, D., Bengs, V., Hüllermeier, E., & Tierney, K. (2020). Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach. In Learning and Intelligent Optimization. LION 2020. (Vol. 12096, pp. 216–232). Cham: Springer. https://doi.org/10.1007/978-3-030-53552-0_22","ama":"El Mesaoudi-Paul A, Weiß D, Bengs V, Hüllermeier E, Tierney K. Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach. In: Learning and Intelligent Optimization. LION 2020. Vol 12096. Lecture Notes in Computer Science. Cham: Springer; 2020:216-232. doi:10.1007/978-3-030-53552-0_22","mla":"El Mesaoudi-Paul, Adil, et al. “Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach.” Learning and Intelligent Optimization. LION 2020., vol. 12096, Springer, 2020, pp. 216–32, doi:10.1007/978-3-030-53552-0_22.","bibtex":"@inbook{El Mesaoudi-Paul_Weiß_Bengs_Hüllermeier_Tierney_2020, place={Cham}, series={Lecture Notes in Computer Science}, title={Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach}, volume={12096}, DOI={10.1007/978-3-030-53552-0_22}, booktitle={Learning and Intelligent Optimization. LION 2020.}, publisher={Springer}, author={El Mesaoudi-Paul, Adil and Weiß, Dimitri and Bengs, Viktor and Hüllermeier, Eyke and Tierney, Kevin}, year={2020}, pages={216–232}, collection={Lecture Notes in Computer Science} }","short":"A. El Mesaoudi-Paul, D. Weiß, V. Bengs, E. Hüllermeier, K. Tierney, in: Learning and Intelligent Optimization. LION 2020., Springer, Cham, 2020, pp. 216–232.","ieee":"A. El Mesaoudi-Paul, D. Weiß, V. Bengs, E. Hüllermeier, and K. Tierney, “Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach,” in Learning and Intelligent Optimization. LION 2020., vol. 12096, Cham: Springer, 2020, pp. 216–232."},"type":"book_chapter","user_id":"76599","publication":"Learning and Intelligent Optimization. LION 2020.","author":[{"last_name":"El Mesaoudi-Paul","full_name":"El Mesaoudi-Paul, Adil","first_name":"Adil"},{"last_name":"Weiß","first_name":"Dimitri","full_name":"Weiß, Dimitri"},{"id":"76599","last_name":"Bengs","full_name":"Bengs, Viktor","first_name":"Viktor"},{"id":"48129","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"},{"full_name":"Tierney, Kevin","first_name":"Kevin","last_name":"Tierney"}],"publisher":"Springer","date_created":"2020-08-17T11:44:37Z","status":"public","volume":12096},{"date_updated":"2022-01-06T06:53:25Z","_id":"18017","year":"2020","type":"preprint","citation":{"short":"A. El Mesaoudi-Paul, V. Bengs, E. Hüllermeier, ArXiv:2002.04275 (n.d.).","ieee":"A. El Mesaoudi-Paul, V. Bengs, and E. Hüllermeier, “Online Preselection with Context Information under the Plackett-Luce Model,” arXiv:2002.04275. .","apa":"El Mesaoudi-Paul, A., Bengs, V., & Hüllermeier, E. (n.d.). Online Preselection with Context Information under the Plackett-Luce Model. ArXiv:2002.04275.","ama":"El Mesaoudi-Paul A, Bengs V, Hüllermeier E. Online Preselection with Context Information under the Plackett-Luce Model. arXiv:200204275.","chicago":"El Mesaoudi-Paul, Adil, Viktor Bengs, and Eyke Hüllermeier. “Online Preselection with Context Information under the Plackett-Luce Model.” ArXiv:2002.04275, n.d.","mla":"El Mesaoudi-Paul, Adil, et al. “Online Preselection with Context Information under the Plackett-Luce Model.” ArXiv:2002.04275.","bibtex":"@article{El Mesaoudi-Paul_Bengs_Hüllermeier, title={Online Preselection with Context Information under the Plackett-Luce Model}, journal={arXiv:2002.04275}, author={El Mesaoudi-Paul, Adil and Bengs, Viktor and Hüllermeier, Eyke} }"},"language":[{"iso":"eng"}],"title":"Online Preselection with Context Information under the Plackett-Luce Model","user_id":"76599","abstract":[{"lang":"eng","text":"We consider an extension of the contextual multi-armed bandit problem, in\r\nwhich, instead of selecting a single alternative (arm), a learner is supposed\r\nto make a preselection in the form of a subset of alternatives. More\r\nspecifically, in each iteration, the learner is presented a set of arms and a\r\ncontext, both described in terms of feature vectors. The task of the learner is\r\nto preselect $k$ of these arms, among which a final choice is made in a second\r\nstep. In our setup, we assume that each arm has a latent (context-dependent)\r\nutility, and that feedback on a preselection is produced according to a\r\nPlackett-Luce model. We propose the CPPL algorithm, which is inspired by the\r\nwell-known UCB algorithm, and evaluate this algorithm on synthetic and real\r\ndata. In particular, we consider an online algorithm selection scenario, which\r\nserved as a main motivation of our problem setting. Here, an instance (which\r\ndefines the context) from a certain problem class (such as SAT) can be solved\r\nby different algorithms (the arms), but only $k$ of these algorithms can\r\nactually be run."}],"publication_status":"draft","date_created":"2020-08-17T11:49:40Z","project":[{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"status":"public","publication":"arXiv:2002.04275","department":[{"_id":"34"},{"_id":"7"},{"_id":"355"}],"author":[{"last_name":"El Mesaoudi-Paul","full_name":"El Mesaoudi-Paul, Adil","first_name":"Adil"},{"first_name":"Viktor","full_name":"Bengs, Viktor","last_name":"Bengs","id":"76599"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","id":"48129","last_name":"Hüllermeier"}]},{"abstract":[{"text":"Algorithm selection (AS) deals with the automatic selection of an algorithm\r\nfrom a fixed set of candidate algorithms most suitable for a specific instance\r\nof an algorithmic problem class, where \"suitability\" often refers to an\r\nalgorithm's runtime. Due to possibly extremely long runtimes of candidate\r\nalgorithms, training data for algorithm selection models is usually generated\r\nunder time constraints in the sense that not all algorithms are run to\r\ncompletion on all instances. Thus, training data usually comprises censored\r\ninformation, as the true runtime of algorithms timed out remains unknown.\r\nHowever, many standard AS approaches are not able to handle such information in\r\na proper way. On the other side, survival analysis (SA) naturally supports\r\ncensored data and offers appropriate ways to use such data for learning\r\ndistributional models of algorithm runtime, as we demonstrate in this work. We\r\nleverage such models as a basis of a sophisticated decision-theoretic approach\r\nto algorithm selection, which we dub Run2Survive. Moreover, taking advantage of\r\na framework of this kind, we advocate a risk-averse approach to algorithm\r\nselection, in which the avoidance of a timeout is given high priority. In an\r\nextensive experimental study with the standard benchmark ASlib, our approach is\r\nshown to be highly competitive and in many cases even superior to\r\nstate-of-the-art AS approaches.","lang":"eng"}],"title":"Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis","user_id":"5786","author":[{"full_name":"Tornede, Alexander","first_name":"Alexander","id":"38209","last_name":"Tornede"},{"last_name":"Wever","id":"33176","first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik"},{"full_name":"Werner, Stefan","first_name":"Stefan","last_name":"Werner"},{"first_name":"Felix","full_name":"Mohr, Felix","last_name":"Mohr"},{"last_name":"Hüllermeier","id":"48129","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}],"publication":"ACML 2020","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"status":"public","project":[{"name":"SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - Project Area B"},{"name":"SFB 901 - Subproject B2","_id":"10"},{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"date_created":"2020-08-25T12:09:28Z","date_updated":"2022-01-06T06:53:28Z","_id":"18276","conference":{"end_date":"2020-11-20","start_date":"2020-11-18","name":"12th Asian Conference on Machine Learning","location":"Bangkok, Thailand"},"main_file_link":[{"url":"https://arxiv.org/pdf/2007.02816.pdf"}],"type":"conference","citation":{"short":"A. Tornede, M.D. Wever, S. Werner, F. Mohr, E. Hüllermeier, in: ACML 2020, 2020.","ieee":"A. Tornede, M. D. Wever, S. Werner, F. Mohr, and E. Hüllermeier, “Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis,” presented at the 12th Asian Conference on Machine Learning, Bangkok, Thailand, 2020.","ama":"Tornede A, Wever MD, Werner S, Mohr F, Hüllermeier E. Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. In: ACML 2020. ; 2020.","apa":"Tornede, A., Wever, M. D., Werner, S., Mohr, F., & Hüllermeier, E. (2020). Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. ACML 2020. 12th Asian Conference on Machine Learning, Bangkok, Thailand.","chicago":"Tornede, Alexander, Marcel Dominik Wever, Stefan Werner, Felix Mohr, and Eyke Hüllermeier. “Run2Survive: A Decision-Theoretic Approach to Algorithm Selection Based on Survival Analysis.” In ACML 2020, 2020.","mla":"Tornede, Alexander, et al. “Run2Survive: A Decision-Theoretic Approach to Algorithm Selection Based on Survival Analysis.” ACML 2020, 2020.","bibtex":"@inproceedings{Tornede_Wever_Werner_Mohr_Hüllermeier_2020, title={Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis}, booktitle={ACML 2020}, author={Tornede, Alexander and Wever, Marcel Dominik and Werner, Stefan and Mohr, Felix and Hüllermeier, Eyke}, year={2020} }"},"year":"2020","language":[{"iso":"eng"}]},{"publication_status":"accepted","status":"public","date_created":"2020-04-19T14:08:06Z","project":[{"name":"SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - Project Area B"},{"_id":"11","name":"SFB 901 - Subproject B3"},{"name":"SFB 901 - Subproject B4","_id":"12"}],"publisher":"Springer","author":[{"id":"50003","last_name":"Richter","full_name":"Richter, Cedric","first_name":"Cedric"},{"id":"48129","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"},{"last_name":"Jakobs","first_name":"Marie-Christine","full_name":"Jakobs, Marie-Christine"},{"last_name":"Wehrheim","id":"573","first_name":"Heike","full_name":"Wehrheim, Heike"}],"publication":"Journal of Automated Software Engineering","department":[{"_id":"7"},{"_id":"77"},{"_id":"355"}],"title":"Algorithm Selection for Software Validation Based on Graph Kernels","user_id":"477","type":"journal_article","year":"2020","citation":{"ieee":"C. Richter, E. Hüllermeier, M.-C. Jakobs, and H. Wehrheim, “Algorithm Selection for Software Validation Based on Graph Kernels,” Journal of Automated Software Engineering.","short":"C. Richter, E. Hüllermeier, M.-C. Jakobs, H. Wehrheim, Journal of Automated Software Engineering (n.d.).","mla":"Richter, Cedric, et al. “Algorithm Selection for Software Validation Based on Graph Kernels.” Journal of Automated Software Engineering, Springer.","bibtex":"@article{Richter_Hüllermeier_Jakobs_Wehrheim, title={Algorithm Selection for Software Validation Based on Graph Kernels}, journal={Journal of Automated Software Engineering}, publisher={Springer}, author={Richter, Cedric and Hüllermeier, Eyke and Jakobs, Marie-Christine and Wehrheim, Heike} }","chicago":"Richter, Cedric, Eyke Hüllermeier, Marie-Christine Jakobs, and Heike Wehrheim. “Algorithm Selection for Software Validation Based on Graph Kernels.” Journal of Automated Software Engineering, n.d.","apa":"Richter, C., Hüllermeier, E., Jakobs, M.-C., & Wehrheim, H. (n.d.). Algorithm Selection for Software Validation Based on Graph Kernels. Journal of Automated Software Engineering.","ama":"Richter C, Hüllermeier E, Jakobs M-C, Wehrheim H. Algorithm Selection for Software Validation Based on Graph Kernels. Journal of Automated Software Engineering."},"language":[{"iso":"eng"}],"_id":"16725","date_updated":"2022-01-06T06:52:55Z"},{"_id":"15629","date_updated":"2022-01-06T06:52:30Z","conference":{"start_date":"2020-04-24","name":"Symposium on Intelligent Data Analysis","location":"Konstanz, Germany","end_date":"2020-04-27"},"year":"2020","citation":{"ieee":"M. D. Wever, A. Tornede, F. Mohr, and E. Hüllermeier, “LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification,” presented at the Symposium on Intelligent Data Analysis, Konstanz, Germany.","short":"M.D. Wever, A. Tornede, F. Mohr, E. Hüllermeier, in: Springer, n.d.","bibtex":"@inproceedings{Wever_Tornede_Mohr_Hüllermeier, title={LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification}, publisher={Springer}, author={Wever, Marcel Dominik and Tornede, Alexander and Mohr, Felix and Hüllermeier, Eyke} }","mla":"Wever, Marcel Dominik, et al. LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification. Springer.","chicago":"Wever, Marcel Dominik, Alexander Tornede, Felix Mohr, and Eyke Hüllermeier. “LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification.” Springer, n.d.","apa":"Wever, M. D., Tornede, A., Mohr, F., & Hüllermeier, E. (n.d.). LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification. Symposium on Intelligent Data Analysis, Konstanz, Germany.","ama":"Wever MD, Tornede A, Mohr F, Hüllermeier E. LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification. In: Springer."},"type":"conference","language":[{"iso":"eng"}],"title":"LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification","user_id":"5786","abstract":[{"lang":"eng","text":"In multi-label classification (MLC), each instance is associated with a set of class labels, in contrast to standard classification where an instance is assigned a single label. Binary relevance (BR) learning, which reduces a multi-label to a set of binary classification problems, one per label, is arguably the most straight-forward approach to MLC. In spite of its simplicity, BR proved to be competitive to more sophisticated MLC methods, and still achieves state-of-the-art performance for many loss functions. Somewhat surprisingly, the optimal choice of the base learner for tackling the binary classification problems has received very little attention so far. Taking advantage of the label independence assumption inherent to BR, we propose a label-wise base learner selection method optimizing label-wise macro averaged performance measures. In an extensive experimental evaluation, we find that or approach, called LiBRe, can significantly improve generalization performance."}],"publication_status":"accepted","status":"public","project":[{"_id":"1","name":"SFB 901"},{"_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"}],"date_created":"2020-01-23T08:44:08Z","author":[{"id":"33176","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik","first_name":"Marcel Dominik"},{"first_name":"Alexander","full_name":"Tornede, Alexander","last_name":"Tornede","id":"38209"},{"last_name":"Mohr","full_name":"Mohr, Felix","first_name":"Felix"},{"last_name":"Hüllermeier","id":"48129","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}],"publisher":"Springer","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}]},{"type":"journal_article","year":"2020","citation":{"ieee":"M. D. Wever, L. van Rooijen, and H. Hamann, “Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets,” Evolutionary Computation, vol. 28, no. 2, pp. 165–193, 2020, doi: 10.1162/evco_a_00266.","short":"M.D. Wever, L. van Rooijen, H. Hamann, Evolutionary Computation 28 (2020) 165–193.","bibtex":"@article{Wever_van Rooijen_Hamann_2020, title={Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets}, volume={28}, DOI={10.1162/evco_a_00266}, number={2}, journal={Evolutionary Computation}, publisher={MIT Press Journals}, author={Wever, Marcel Dominik and van Rooijen, Lorijn and Hamann, Heiko}, year={2020}, pages={165–193} }","mla":"Wever, Marcel Dominik, et al. “Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets.” Evolutionary Computation, vol. 28, no. 2, MIT Press Journals, 2020, pp. 165–193, doi:10.1162/evco_a_00266.","apa":"Wever, M. D., van Rooijen, L., & Hamann, H. (2020). Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets. Evolutionary Computation, 28(2), 165–193. https://doi.org/10.1162/evco_a_00266","ama":"Wever MD, van Rooijen L, Hamann H. Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets. Evolutionary Computation. 2020;28(2):165–193. doi:10.1162/evco_a_00266","chicago":"Wever, Marcel Dominik, Lorijn van Rooijen, and Heiko Hamann. “Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets.” Evolutionary Computation 28, no. 2 (2020): 165–193. https://doi.org/10.1162/evco_a_00266."},"page":"165–193","intvolume":" 28","_id":"15025","issue":"2","publisher":"MIT Press Journals","author":[{"last_name":"Wever","id":"33176","first_name":"Marcel Dominik","full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818"},{"last_name":"van Rooijen","id":"58843","first_name":"Lorijn","full_name":"van Rooijen, Lorijn"},{"full_name":"Hamann, Heiko","first_name":"Heiko","last_name":"Hamann"}],"publication":"Evolutionary Computation","status":"public","date_created":"2019-11-18T14:19:19Z","volume":28,"abstract":[{"text":"In software engineering, the imprecise requirements of a user are transformed to a formal requirements specification during the requirements elicitation process. This process is usually guided by requirements engineers interviewing the user. We want to partially automate this first step of the software engineering process in order to enable users to specify a desired software system on their own. With our approach, users are only asked to provide exemplary behavioral descriptions. The problem of synthesizing a requirements specification from examples can partially be reduced to the problem of grammatical inference, to which we apply an active coevolutionary learning approach. However, this approach would usually require many feedback queries to be sent to the user. In this work, we extend and generalize our active learning approach to receive knowledge from multiple oracles, also known as proactive learning. The ‘user oracle’ represents input received from the user and the ‘knowledge oracle’ represents available, formalized domain knowledge. We call our two-oracle approach the ‘first apply knowledge then query’ (FAKT/Q) algorithm. We compare FAKT/Q to the active learning approach and provide an extensive benchmark evaluation. As result we find that the number of required user queries is reduced and the inference process is sped up significantly. Finally, with so-called On-The-Fly Markets, we present a motivation and an application of our approach where such knowledge is available.","lang":"eng"}],"user_id":"15415","language":[{"iso":"eng"}],"date_updated":"2022-01-06T06:52:15Z","doi":"10.1162/evco_a_00266","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"},{"_id":"63"},{"_id":"238"}],"project":[{"name":"SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - Project Area B"},{"name":"SFB 901 - Subproject B1","_id":"9"},{"name":"SFB 901 - Subproject B2","_id":"10"},{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"publication_status":"published","related_material":{"link":[{"url":"https://www.mitpressjournals.org/doi/pdf/10.1162/evco_a_00266","relation":"confirmation"}]},"title":"Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets"},{"_id":"19523","date_updated":"2022-01-06T06:54:06Z","language":[{"iso":"eng"}],"year":"2019","citation":{"bibtex":"@article{Pfannschmidt_Gupta_Hüllermeier_2019, title={Learning Choice Functions: Concepts and Architectures}, journal={arXiv:1901.10860}, author={Pfannschmidt, Karlson and Gupta, Pritha and Hüllermeier, Eyke}, year={2019} }","mla":"Pfannschmidt, Karlson, et al. “Learning Choice Functions: Concepts and Architectures.” ArXiv:1901.10860, 2019.","apa":"Pfannschmidt, K., Gupta, P., & Hüllermeier, E. (2019). Learning Choice Functions: Concepts and Architectures. ArXiv:1901.10860.","ama":"Pfannschmidt K, Gupta P, Hüllermeier E. Learning Choice Functions: Concepts and Architectures. arXiv:190110860. 2019.","chicago":"Pfannschmidt, Karlson, Pritha Gupta, and Eyke Hüllermeier. “Learning Choice Functions: Concepts and Architectures.” ArXiv:1901.10860, 2019.","ieee":"K. Pfannschmidt, P. Gupta, and E. Hüllermeier, “Learning Choice Functions: Concepts and Architectures,” arXiv:1901.10860. 2019.","short":"K. Pfannschmidt, P. Gupta, E. Hüllermeier, ArXiv:1901.10860 (2019)."},"type":"preprint","user_id":"13472","title":"Learning Choice Functions: Concepts and Architectures","abstract":[{"text":"We study the problem of learning choice functions, which play an important\r\nrole in various domains of application, most notably in the field of economics.\r\nFormally, a choice function is a mapping from sets to sets: Given a set of\r\nchoice alternatives as input, a choice function identifies a subset of most\r\npreferred elements. Learning choice functions from suitable training data comes\r\nwith a number of challenges. For example, the sets provided as input and the\r\nsubsets produced as output can be of any size. Moreover, since the order in\r\nwhich alternatives are presented is irrelevant, a choice function should be\r\nsymmetric. Perhaps most importantly, choice functions are naturally\r\ncontext-dependent, in the sense that the preference in favor of an alternative\r\nmay depend on what other options are available. We formalize the problem of\r\nlearning choice functions and present two general approaches based on two\r\nrepresentations of context-dependent utility functions. Both approaches are\r\ninstantiated by means of appropriate neural network architectures, and their\r\nperformance is demonstrated on suitable benchmark tasks.","lang":"eng"}],"status":"public","project":[{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"date_created":"2020-09-17T10:53:38Z","author":[{"full_name":"Pfannschmidt, Karlson","first_name":"Karlson","last_name":"Pfannschmidt"},{"full_name":"Gupta, Pritha","first_name":"Pritha","last_name":"Gupta"},{"last_name":"Hüllermeier","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}],"department":[{"_id":"7"},{"_id":"355"}],"publication":"arXiv:1901.10860"},{"page":"124-146","citation":{"mla":"Merten, Marie-Luis, et al. “Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff.” Niederdeutsches Jahrbuch, no. 142, 2019, pp. 124–46.","bibtex":"@article{Merten_Seemann_Wever_2019, title={Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff}, number={142}, journal={Niederdeutsches Jahrbuch}, author={Merten, Marie-Luis and Seemann, Nina and Wever, Marcel Dominik}, year={2019}, pages={124–146} }","chicago":"Merten, Marie-Luis, Nina Seemann, and Marcel Dominik Wever. “Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff.” Niederdeutsches Jahrbuch, no. 142 (2019): 124–46.","ama":"Merten M-L, Seemann N, Wever MD. Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff. Niederdeutsches Jahrbuch. 2019;(142):124-146.","apa":"Merten, M.-L., Seemann, N., & Wever, M. D. (2019). Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff. Niederdeutsches Jahrbuch, 142, 124–146.","ieee":"M.-L. Merten, N. Seemann, and M. D. Wever, “Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff,” Niederdeutsches Jahrbuch, no. 142, pp. 124–146, 2019.","short":"M.-L. Merten, N. Seemann, M.D. Wever, Niederdeutsches Jahrbuch (2019) 124–146."},"year":"2019","type":"journal_article","language":[{"iso":"ger"}],"_id":"17565","date_updated":"2022-01-06T06:53:15Z","issue":"142","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"publication":"Niederdeutsches Jahrbuch","author":[{"first_name":"Marie-Luis","full_name":"Merten, Marie-Luis","last_name":"Merten"},{"last_name":"Seemann","first_name":"Nina","full_name":"Seemann, Nina"},{"last_name":"Wever","id":"33176","first_name":"Marcel Dominik","full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818"}],"publication_status":"published","project":[{"_id":"39","name":"InterGramm"}],"date_created":"2020-08-03T13:55:04Z","status":"public","title":"Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff","user_id":"5786"},{"_id":"18018","date_updated":"2022-01-06T06:53:25Z","year":"2019","citation":{"ieee":"V. Bengs and H. Holzmann, “Uniform approximation in classical weak convergence theory,” arXiv:1903.09864. 2019.","short":"V. Bengs, H. Holzmann, ArXiv:1903.09864 (2019).","mla":"Bengs, Viktor, and Hajo Holzmann. “Uniform Approximation in Classical Weak Convergence Theory.” ArXiv:1903.09864, 2019.","bibtex":"@article{Bengs_Holzmann_2019, title={Uniform approximation in classical weak convergence theory}, journal={arXiv:1903.09864}, author={Bengs, Viktor and Holzmann, Hajo}, year={2019} }","chicago":"Bengs, Viktor, and Hajo Holzmann. “Uniform Approximation in Classical Weak Convergence Theory.” ArXiv:1903.09864, 2019.","apa":"Bengs, V., & Holzmann, H. (2019). Uniform approximation in classical weak convergence theory. ArXiv:1903.09864.","ama":"Bengs V, Holzmann H. Uniform approximation in classical weak convergence theory. arXiv:190309864. 2019."},"type":"preprint","user_id":"76599","title":"Uniform approximation in classical weak convergence theory","abstract":[{"text":"A common statistical task lies in showing asymptotic normality of certain\nstatistics. In many of these situations, classical textbook results on weak\nconvergence theory suffice for the problem at hand. However, there are quite\nsome scenarios where stronger results are needed in order to establish an\nasymptotic normal approximation uniformly over a family of probability\nmeasures. In this note we collect some results in this direction. We restrict\nourselves to weak convergence in $\\mathbb R^d$ with continuous limit measures.","lang":"eng"}],"date_created":"2020-08-17T12:10:55Z","status":"public","publication":"arXiv:1903.09864","department":[{"_id":"34"},{"_id":"7"},{"_id":"355"}],"author":[{"last_name":"Bengs","first_name":"Viktor","full_name":"Bengs, Viktor"},{"first_name":"Hajo","full_name":"Holzmann, Hajo","last_name":"Holzmann"}]},{"file":[{"access_level":"closed","date_created":"2019-04-10T07:17:17Z","file_name":"Towards_Automated_Machine_Learning_for_Multi_Label_Classification.pdf","date_updated":"2019-04-10T07:17:17Z","content_type":"application/pdf","relation":"main_file","success":1,"file_size":"74484","file_id":"8870","creator":"wever"}],"department":[{"_id":"355"}],"file_date_updated":"2019-04-10T07:17:17Z","author":[{"full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik","id":"33176","last_name":"Wever"},{"full_name":"Mohr, Felix","first_name":"Felix","last_name":"Mohr"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","id":"48129"},{"full_name":"Hetzer, Alexander","first_name":"Alexander","id":"38209","last_name":"Hetzer"}],"date_created":"2019-04-10T07:17:55Z","project":[{"name":"SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - Project Area B"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"status":"public","has_accepted_license":"1","user_id":"49109","title":"Towards Automated Machine Learning for Multi-Label Classification","ddc":["000"],"language":[{"iso":"eng"}],"citation":{"short":"M.D. Wever, F. Mohr, E. Hüllermeier, A. Hetzer, in: 2019.","ieee":"M. D. Wever, F. Mohr, E. Hüllermeier, and A. Hetzer, “Towards Automated Machine Learning for Multi-Label Classification,” presented at the European Conference on Data Analytics (ECDA), Bayreuth, Germany, 2019.","ama":"Wever MD, Mohr F, Hüllermeier E, Hetzer A. Towards Automated Machine Learning for Multi-Label Classification. In: ; 2019.","apa":"Wever, M. D., Mohr, F., Hüllermeier, E., & Hetzer, A. (2019). Towards Automated Machine Learning for Multi-Label Classification. Presented at the European Conference on Data Analytics (ECDA), Bayreuth, Germany.","chicago":"Wever, Marcel Dominik, Felix Mohr, Eyke Hüllermeier, and Alexander Hetzer. “Towards Automated Machine Learning for Multi-Label Classification,” 2019.","mla":"Wever, Marcel Dominik, et al. Towards Automated Machine Learning for Multi-Label Classification. 2019.","bibtex":"@inproceedings{Wever_Mohr_Hüllermeier_Hetzer_2019, title={Towards Automated Machine Learning for Multi-Label Classification}, author={Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke and Hetzer, Alexander}, year={2019} }"},"type":"conference_abstract","year":"2019","conference":{"end_date":"2019-03-20","location":"Bayreuth, Germany","start_date":"2019-03-18","name":"European Conference on Data Analytics (ECDA)"},"date_updated":"2022-01-06T07:04:04Z","_id":"8868"},{"title":"Choice Functions Generated by Mallows and Plackett–Luce Relations","user_id":"315","volume":15,"date_created":"2019-07-08T15:34:03Z","status":"public","department":[{"_id":"34"},{"_id":"355"},{"_id":"7"}],"publication":"New Mathematics and Natural Computation","author":[{"first_name":"V. K.","full_name":"Tagne, V. K.","last_name":"Tagne"},{"first_name":"S.","full_name":"Fotso, S.","last_name":"Fotso"},{"last_name":"Fono","full_name":"Fono, L. A. ","first_name":"L. A. "},{"last_name":"Hüllermeier","id":"48129","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}],"issue":"2","intvolume":" 15","_id":"10578","date_updated":"2022-01-06T06:50:45Z","page":"191-213","citation":{"short":"V.K. Tagne, S. Fotso, L.A. Fono, E. Hüllermeier, New Mathematics and Natural Computation 15 (2019) 191–213.","ieee":"V. K. Tagne, S. Fotso, L. A. Fono, and E. Hüllermeier, “Choice Functions Generated by Mallows and Plackett–Luce Relations,” New Mathematics and Natural Computation, vol. 15, no. 2, pp. 191–213, 2019.","apa":"Tagne, V. K., Fotso, S., Fono, L. A., & Hüllermeier, E. (2019). Choice Functions Generated by Mallows and Plackett–Luce Relations. New Mathematics and Natural Computation, 15(2), 191–213.","ama":"Tagne VK, Fotso S, Fono LA, Hüllermeier E. Choice Functions Generated by Mallows and Plackett–Luce Relations. New Mathematics and Natural Computation. 2019;15(2):191-213.","chicago":"Tagne, V. K., S. Fotso, L. A. Fono, and Eyke Hüllermeier. “Choice Functions Generated by Mallows and Plackett–Luce Relations.” New Mathematics and Natural Computation 15, no. 2 (2019): 191–213.","mla":"Tagne, V. K., et al. “Choice Functions Generated by Mallows and Plackett–Luce Relations.” New Mathematics and Natural Computation, vol. 15, no. 2, 2019, pp. 191–213.","bibtex":"@article{Tagne_Fotso_Fono_Hüllermeier_2019, title={Choice Functions Generated by Mallows and Plackett–Luce Relations}, volume={15}, number={2}, journal={New Mathematics and Natural Computation}, author={Tagne, V. K. and Fotso, S. and Fono, L. A. and Hüllermeier, Eyke}, year={2019}, pages={191–213} }"},"type":"journal_article","year":"2019","language":[{"iso":"eng"}]},{"page":"31-44","citation":{"mla":"Couso, Ines, et al. “Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning.” IEEE Computational Intelligence Magazine, 2019, pp. 31–44, doi:10.1109/mci.2018.2881642.","bibtex":"@article{Couso_Borgelt_Hüllermeier_Kruse_2019, title={Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning}, DOI={10.1109/mci.2018.2881642}, journal={IEEE Computational Intelligence Magazine}, author={Couso, Ines and Borgelt, Christian and Hüllermeier, Eyke and Kruse, Rudolf}, year={2019}, pages={31–44} }","chicago":"Couso, Ines, Christian Borgelt, Eyke Hüllermeier, and Rudolf Kruse. “Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning.” IEEE Computational Intelligence Magazine, 2019, 31–44. https://doi.org/10.1109/mci.2018.2881642.","ama":"Couso I, Borgelt C, Hüllermeier E, Kruse R. 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Kruse, IEEE Computational Intelligence Magazine (2019) 31–44."},"type":"journal_article","year":"2019","language":[{"iso":"eng"}],"_id":"15001","date_updated":"2022-01-06T06:52:13Z","doi":"10.1109/mci.2018.2881642","publication":"IEEE Computational Intelligence Magazine","department":[{"_id":"34"},{"_id":"355"}],"author":[{"full_name":"Couso, Ines","first_name":"Ines","last_name":"Couso"},{"full_name":"Borgelt, Christian","first_name":"Christian","last_name":"Borgelt"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","id":"48129","last_name":"Hüllermeier"},{"last_name":"Kruse","first_name":"Rudolf","full_name":"Kruse, Rudolf"}],"publication_identifier":{"issn":["1556-603X","1556-6048"]},"publication_status":"published","date_created":"2019-11-15T10:11:37Z","status":"public","title":"Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning","user_id":"315"},{"language":[{"iso":"eng"}],"date_updated":"2022-01-06T06:52:14Z","oa":"1","doi":"10.1007/s10618-018-0595-5","department":[{"_id":"34"},{"_id":"355"}],"publication_identifier":{"issn":["1573-756X"]},"title":"Multi-target prediction: a unifying view on problems and methods","year":"2019","type":"journal_article","citation":{"apa":"Waegeman, W., Dembczynski, K., & Hüllermeier, E. (2019). Multi-target prediction: a unifying view on problems and methods. Data Mining and Knowledge Discovery, 33(2), 293–324. https://doi.org/10.1007/s10618-018-0595-5","ama":"Waegeman W, Dembczynski K, Hüllermeier E. Multi-target prediction: a unifying view on problems and methods. 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Hüllermeier, Data Mining and Knowledge Discovery 33 (2019) 293–324.","ieee":"W. Waegeman, K. Dembczynski, and E. Hüllermeier, “Multi-target prediction: a unifying view on problems and methods,” Data Mining and Knowledge Discovery, vol. 33, no. 2, pp. 293–324, 2019."},"page":"293-324","intvolume":" 33","_id":"15002","issue":"2","file":[{"date_updated":"2020-02-28T12:45:26Z","content_type":"application/pdf","relation":"main_file","file_size":837808,"creator":"lettmann","file_id":"16155","access_level":"open_access","file_name":"multi-target-prediction.pdf","date_created":"2020-02-28T12:43:39Z"}],"author":[{"full_name":"Waegeman, Willem","first_name":"Willem","last_name":"Waegeman"},{"full_name":"Dembczynski, Krzysztof","first_name":"Krzysztof","last_name":"Dembczynski"},{"id":"48129","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"publication":"Data Mining and Knowledge Discovery","file_date_updated":"2020-02-28T12:45:26Z","has_accepted_license":"1","status":"public","date_created":"2019-11-15T10:16:34Z","volume":33,"abstract":[{"lang":"eng","text":"Many problem settings in machine learning are concerned with the simultaneous prediction of multiple target variables of diverse type. Amongst others, such problem settings arise in multivariate regression, multi-label classification, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion. These subfields of machine learning are typically studied in isolation, without highlighting or exploring important relationships. In this paper, we present a unifying view on what we call multi-target prediction (MTP) problems and methods. First, we formally discuss commonalities and differences between existing MTP problems. To this end, we introduce a general framework that covers the above subfields as special cases. As a second contribution, we provide a structured overview of MTP methods. This is accomplished by identifying a number of key properties, which distinguish such methods and determine their suitability for different types of problems. Finally, we also discuss a few challenges for future research."}],"user_id":"315","ddc":["000"]},{"language":[{"iso":"eng"}],"year":"2019","citation":{"bibtex":"@inproceedings{Mortier_Wydmuch_Dembczynski_Hüllermeier_Waegeman_2019, title={Set-Valued Prediction in Multi-Class Classification}, booktitle={Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8, 2019}, author={Mortier, Thomas and Wydmuch, Marek and Dembczynski, Krzysztof and Hüllermeier, Eyke and Waegeman, Willem}, year={2019} }","mla":"Mortier, Thomas, et al. “Set-Valued Prediction in Multi-Class Classification.” Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8, 2019, 2019.","ama":"Mortier T, Wydmuch M, Dembczynski K, Hüllermeier E, Waegeman W. Set-Valued Prediction in Multi-Class Classification. In: Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8, 2019. ; 2019.","apa":"Mortier, T., Wydmuch, M., Dembczynski, K., Hüllermeier, E., & Waegeman, W. (2019). Set-Valued Prediction in Multi-Class Classification. In Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8, 2019.","chicago":"Mortier, Thomas, Marek Wydmuch, Krzysztof Dembczynski, Eyke Hüllermeier, and Willem Waegeman. “Set-Valued Prediction in Multi-Class Classification.” In Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8, 2019, 2019.","ieee":"T. Mortier, M. Wydmuch, K. Dembczynski, E. Hüllermeier, and W. Waegeman, “Set-Valued Prediction in Multi-Class Classification,” in Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8, 2019, 2019.","short":"T. Mortier, M. Wydmuch, K. Dembczynski, E. Hüllermeier, W. Waegeman, in: Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8, 2019, 2019."},"type":"conference","date_updated":"2022-01-06T06:52:14Z","_id":"15003","author":[{"full_name":"Mortier, Thomas","first_name":"Thomas","last_name":"Mortier"},{"full_name":"Wydmuch, Marek","first_name":"Marek","last_name":"Wydmuch"},{"last_name":"Dembczynski","full_name":"Dembczynski, Krzysztof","first_name":"Krzysztof"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","id":"48129"},{"last_name":"Waegeman","first_name":"Willem","full_name":"Waegeman, Willem"}],"publication":"Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8, 2019","department":[{"_id":"34"},{"_id":"355"}],"status":"public","date_created":"2019-11-15T10:20:55Z","user_id":"315","title":"Set-Valued Prediction in Multi-Class Classification"},{"citation":{"mla":"Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Feature Selection for Analogy-Based Learning to Rank.” Discovery Science, 2019, doi:10.1007/978-3-030-33778-0_22.","bibtex":"@inbook{Ahmadi Fahandar_Hüllermeier_2019, place={Cham}, title={Feature Selection for Analogy-Based Learning to Rank}, DOI={10.1007/978-3-030-33778-0_22}, booktitle={Discovery Science}, author={Ahmadi Fahandar, Mohsen and Hüllermeier, Eyke}, year={2019} }","apa":"Ahmadi Fahandar, M., & Hüllermeier, E. 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Yet, there is only little work on other types of machine learning problems so far. In particular, there is almost no work on automating the engineering of machine learning solutions for multi-label classification (MLC). We show how the scope of ML-Plan, an AutoML-tool for multi-class classification, can be extended towards MLC using MEKA, which is a multi-label extension of the well-known Java library WEKA. The resulting approach recursively refines MEKA's multi-label classifiers, nesting other multi-label classifiers for meta algorithms and single-label classifiers provided by WEKA as base learners. In our evaluation, we find that the proposed approach yields strong results and performs significantly better than a set of baselines we compare with."}],"language":[{"iso":"eng"}],"citation":{"bibtex":"@inproceedings{Wever_Mohr_Tornede_Hüllermeier_2019, title={Automating Multi-Label Classification Extending ML-Plan}, author={Wever, Marcel Dominik and Mohr, Felix and Tornede, Alexander and Hüllermeier, Eyke}, year={2019} }","mla":"Wever, Marcel Dominik, et al. Automating Multi-Label Classification Extending ML-Plan. 2019.","apa":"Wever, M. D., Mohr, F., Tornede, A., & Hüllermeier, E. (2019). Automating Multi-Label Classification Extending ML-Plan. Presented at the 6th ICML Workshop on Automated Machine Learning (AutoML 2019), Long Beach, CA, USA.","ama":"Wever MD, Mohr F, Tornede A, Hüllermeier E. Automating Multi-Label Classification Extending ML-Plan. 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Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches. IEEE Transactions on Cognitive and Developmental Systems. https://doi.org/10.1109/TCDS.2019.2892991","ama":"Rohlfing K, Leonardi G, Nomikou I, Rączaszek-Leonardi J, Hüllermeier E. Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches. IEEE Transactions on Cognitive and Developmental Systems. Published online 2019. doi:10.1109/TCDS.2019.2892991","chicago":"Rohlfing, Katharina, Giuseppe Leonardi, Iris Nomikou, Joanna Rączaszek-Leonardi, and Eyke Hüllermeier. “Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches.” IEEE Transactions on Cognitive and Developmental Systems, 2019. https://doi.org/10.1109/TCDS.2019.2892991.","mla":"Rohlfing, Katharina, et al. “Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches.” IEEE Transactions on Cognitive and Developmental Systems, 2019, doi:10.1109/TCDS.2019.2892991.","bibtex":"@article{Rohlfing_Leonardi_Nomikou_Rączaszek-Leonardi_Hüllermeier_2019, title={Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches}, DOI={10.1109/TCDS.2019.2892991}, journal={IEEE Transactions on Cognitive and Developmental Systems}, author={Rohlfing, Katharina and Leonardi, Giuseppe and Nomikou, Iris and Rączaszek-Leonardi, Joanna and Hüllermeier, Eyke}, year={2019} }","short":"K. Rohlfing, G. Leonardi, I. Nomikou, J. Rączaszek-Leonardi, E. Hüllermeier, IEEE Transactions on Cognitive and Developmental Systems (2019).","ieee":"K. Rohlfing, G. Leonardi, I. Nomikou, J. Rączaszek-Leonardi, and E. Hüllermeier, “Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches,” IEEE Transactions on Cognitive and Developmental Systems, 2019, doi: 10.1109/TCDS.2019.2892991."},"language":[{"iso":"eng"}],"title":"Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches","user_id":"14931","date_created":"2020-11-02T13:25:49Z","status":"public","department":[{"_id":"749"},{"_id":"355"}],"publication":"IEEE Transactions on Cognitive and Developmental Systems","author":[{"last_name":"Rohlfing","id":"50352","first_name":"Katharina","full_name":"Rohlfing, Katharina"},{"full_name":"Leonardi, Giuseppe","first_name":"Giuseppe","last_name":"Leonardi"},{"last_name":"Nomikou","first_name":"Iris","full_name":"Nomikou, Iris"},{"full_name":"Rączaszek-Leonardi, Joanna","first_name":"Joanna","last_name":"Rączaszek-Leonardi"},{"id":"48129","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}]}]