[{"publication":"International Journal of Approximate Reasoning","type":"journal_article","status":"public","user_id":"44040","_id":"21636","language":[{"iso":"eng"}],"citation":{"bibtex":"@article{Lienen_Hüllermeier_2021, title={Instance weighting through data imprecisiation}, journal={International Journal of Approximate Reasoning}, publisher={Elsevier}, author={Lienen, Julian and Hüllermeier, Eyke}, year={2021} }","mla":"Lienen, Julian, and Eyke Hüllermeier. “Instance Weighting through Data Imprecisiation.” <i>International Journal of Approximate Reasoning</i>, Elsevier, 2021.","short":"J. Lienen, E. Hüllermeier, International Journal of Approximate Reasoning (2021).","apa":"Lienen, J., &#38; Hüllermeier, E. (2021). Instance weighting through data imprecisiation. <i>International Journal of Approximate Reasoning</i>.","ama":"Lienen J, Hüllermeier E. Instance weighting through data imprecisiation. <i>International Journal of Approximate Reasoning</i>. 2021.","chicago":"Lienen, Julian, and Eyke Hüllermeier. “Instance Weighting through Data Imprecisiation.” <i>International Journal of Approximate Reasoning</i>, 2021.","ieee":"J. Lienen and E. Hüllermeier, “Instance weighting through data imprecisiation,” <i>International Journal of Approximate Reasoning</i>, 2021."},"year":"2021","date_created":"2021-04-20T06:48:18Z","author":[{"last_name":"Lienen","id":"44040","full_name":"Lienen, Julian","first_name":"Julian"},{"first_name":"Eyke","id":"48129","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier"}],"date_updated":"2022-01-06T06:55:08Z","publisher":"Elsevier","main_file_link":[{"url":"https://www.sciencedirect.com/science/article/pii/S0888613X21000463"}],"title":"Instance weighting through data imprecisiation"},{"main_file_link":[{"open_access":"1","url":"https://ojs.aaai.org/index.php/AAAI/article/view/17041"}],"conference":{"name":"35th AAAI Conference on Artificial Intelligence, AAAI","start_date":"2021-02-02","end_date":"2021-02-09","location":"Online"},"title":"From Label Smoothing to Label Relaxation","author":[{"full_name":"Lienen, Julian","id":"44040","last_name":"Lienen","first_name":"Julian"},{"last_name":"Hüllermeier","id":"48129","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"date_created":"2021-04-20T06:50:43Z","volume":35,"date_updated":"2022-01-06T06:55:08Z","publisher":"AAAI Press","oa":"1","citation":{"apa":"Lienen, J., &#38; Hüllermeier, E. (2021). From Label Smoothing to Label Relaxation. In <i>Proceedings of the 35th AAAI Conference on Artificial Intelligence, AAAI</i> (Vol. 35, pp. 8583–8591). Online: AAAI Press.","mla":"Lienen, Julian, and Eyke Hüllermeier. “From Label Smoothing to Label Relaxation.” <i>Proceedings of the 35th AAAI Conference on Artificial Intelligence, AAAI</i>, vol. 35, no. 10, AAAI Press, 2021, pp. 8583–91.","bibtex":"@inproceedings{Lienen_Hüllermeier_2021, title={From Label Smoothing to Label Relaxation}, volume={35}, number={10}, booktitle={Proceedings of the 35th AAAI Conference on Artificial Intelligence, AAAI}, publisher={AAAI Press}, author={Lienen, Julian and Hüllermeier, Eyke}, year={2021}, pages={8583–8591} }","short":"J. Lienen, E. Hüllermeier, in: Proceedings of the 35th AAAI Conference on Artificial Intelligence, AAAI, AAAI Press, 2021, pp. 8583–8591.","ama":"Lienen J, Hüllermeier E. From Label Smoothing to Label Relaxation. In: <i>Proceedings of the 35th AAAI Conference on Artificial Intelligence, AAAI</i>. Vol 35. AAAI Press; 2021:8583-8591.","ieee":"J. Lienen and E. Hüllermeier, “From Label Smoothing to Label Relaxation,” in <i>Proceedings of the 35th AAAI Conference on Artificial Intelligence, AAAI</i>, Online, 2021, vol. 35, no. 10, pp. 8583–8591.","chicago":"Lienen, Julian, and Eyke Hüllermeier. “From Label Smoothing to Label Relaxation.” In <i>Proceedings of the 35th AAAI Conference on Artificial Intelligence, AAAI</i>, 35:8583–91. AAAI Press, 2021."},"intvolume":"        35","page":"8583-8591","year":"2021","issue":"10","language":[{"iso":"eng"}],"user_id":"44040","_id":"21637","status":"public","type":"conference","publication":"Proceedings of the 35th AAAI Conference on Artificial Intelligence, AAAI"},{"publication_status":"epub_ahead","quality_controlled":"1","citation":{"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., &#38; Soltenborn, C. (2021). A Meta-Review on Artificial Intelligence in Product Creation. <i>Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21)</i>. 30th International Joint Conference on Artificial Intelligence (IJCAI 2021) - Workshop “AI and Product Design,” Montreal, Kanada.","mla":"Bernijazov, Ruslan, et al. “A Meta-Review on Artificial Intelligence in Product Creation.” <i>Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21)</i>, 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} }","ieee":"R. Bernijazov <i>et al.</i>, “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.","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 <i>Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21)</i>, 2021.","ama":"Bernijazov R, Dicks A, Dumitrescu R, et al. A Meta-Review on Artificial Intelligence in Product Creation. In: <i>Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21)</i>. ; 2021."},"year":"2021","date_created":"2021-09-06T08:23:45Z","author":[{"first_name":"Ruslan","full_name":"Bernijazov, Ruslan","last_name":"Bernijazov"},{"full_name":"Dicks, Alexander","last_name":"Dicks","first_name":"Alexander"},{"id":"16190","full_name":"Dumitrescu, Roman","last_name":"Dumitrescu","first_name":"Roman"},{"full_name":"Foullois, Marc","last_name":"Foullois","first_name":"Marc"},{"first_name":"Jonas Manuel","id":"43980","full_name":"Hanselle, Jonas Manuel","last_name":"Hanselle","orcid":"0000-0002-1231-4985"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","id":"48129","last_name":"Hüllermeier"},{"full_name":"Karakaya, Gökce","last_name":"Karakaya","first_name":"Gökce"},{"last_name":"Ködding","full_name":"Ködding, Patrick","id":"45402","first_name":"Patrick"},{"last_name":"Lohweg","full_name":"Lohweg, Volker","first_name":"Volker"},{"first_name":"Manuel","last_name":"Malatyali","id":"41265","full_name":"Malatyali, Manuel"},{"id":"15523","full_name":"Meyer auf der Heide, Friedhelm","last_name":"Meyer auf der Heide","first_name":"Friedhelm"},{"first_name":"Melina","full_name":"Panzner, Melina","last_name":"Panzner"},{"orcid":"0000-0002-0342-8227","last_name":"Soltenborn","id":"1737","full_name":"Soltenborn, Christian","first_name":"Christian"}],"date_updated":"2022-01-06T06:55:59Z","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"}],"conference":{"location":"Montreal, Kanada","end_date":"2021-08-26","start_date":"2021-08-19","name":"30th International Joint Conference on Artificial Intelligence (IJCAI 2021) - Workshop \"AI and Product Design\""},"title":"A Meta-Review on Artificial Intelligence in Product Creation","type":"conference","publication":"Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21)","status":"public","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."},{"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.","lang":"eng"}],"user_id":"15415","department":[{"_id":"63"},{"_id":"563"},{"_id":"355"},{"_id":"241"}],"_id":"23779","language":[{"iso":"eng"}],"keyword":["Artificial Intelligence Product Creation Literature Review"]},{"language":[{"iso":"eng"}],"_id":"22280","user_id":"44040","status":"public","type":"conference","publication":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR","title":"Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce Model","conference":{"end_date":"2021-06-25","location":"Online","name":"IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR","start_date":"2021-06-19"},"date_updated":"2022-01-06T06:55:29Z","date_created":"2021-06-02T10:35:40Z","author":[{"first_name":"Julian","full_name":"Lienen, Julian","id":"44040","last_name":"Lienen"},{"first_name":"Eyke","last_name":"Hüllermeier","id":"48129","full_name":"Hüllermeier, Eyke"},{"first_name":"Ralph","full_name":"Ewerth, Ralph","last_name":"Ewerth"},{"full_name":"Nommensen, Nils","last_name":"Nommensen","first_name":"Nils"}],"year":"2021","citation":{"mla":"Lienen, Julian, et al. “Monocular Depth Estimation via Listwise Ranking Using the Plackett-Luce Model.” <i>Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR</i>, 2021, pp. 14595–604.","bibtex":"@inproceedings{Lienen_Hüllermeier_Ewerth_Nommensen_2021, title={Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce Model}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR}, author={Lienen, Julian and Hüllermeier, Eyke and Ewerth, Ralph and Nommensen, Nils}, year={2021}, pages={14595–14604} }","short":"J. Lienen, E. Hüllermeier, R. Ewerth, N. Nommensen, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 2021, pp. 14595–14604.","apa":"Lienen, J., Hüllermeier, E., Ewerth, R., &#38; Nommensen, N. (2021). Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce Model. <i>Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR</i>, 14595–14604.","ama":"Lienen J, Hüllermeier E, Ewerth R, Nommensen N. Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce Model. In: <i>Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR</i>. ; 2021:14595-14604.","ieee":"J. Lienen, E. Hüllermeier, R. Ewerth, and N. Nommensen, “Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce Model,” in <i>Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR</i>, Online, 2021, pp. 14595–14604.","chicago":"Lienen, Julian, Eyke Hüllermeier, Ralph Ewerth, and Nils Nommensen. “Monocular Depth Estimation via Listwise Ranking Using the Plackett-Luce Model.” In <i>Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR</i>, 14595–604, 2021."},"page":"14595-14604"},{"author":[{"first_name":"Julian","last_name":"Lienen","id":"44040","full_name":"Lienen, Julian"},{"full_name":"Hüllermeier, Eyke","id":"48129","last_name":"Hüllermeier","first_name":"Eyke"}],"date_created":"2021-06-23T07:24:38Z","date_updated":"2022-01-06T06:55:35Z","oa":"1","main_file_link":[{"url":"https://arxiv.org/pdf/2106.11853.pdf","open_access":"1"}],"title":"Credal Self-Supervised Learning","citation":{"apa":"Lienen, J., &#38; Hüllermeier, E. (2021). Credal Self-Supervised Learning. <i>ArXiv:2106.11853</i>.","mla":"Lienen, Julian, and Eyke Hüllermeier. “Credal Self-Supervised Learning.” <i>ArXiv:2106.11853</i>, 2021.","bibtex":"@article{Lienen_Hüllermeier_2021, title={Credal Self-Supervised Learning}, journal={arXiv:2106.11853}, author={Lienen, Julian and Hüllermeier, Eyke}, year={2021} }","short":"J. Lienen, E. Hüllermeier, ArXiv:2106.11853 (2021).","ama":"Lienen J, Hüllermeier E. Credal Self-Supervised Learning. <i>arXiv:210611853</i>. 2021.","chicago":"Lienen, Julian, and Eyke Hüllermeier. “Credal Self-Supervised Learning.” <i>ArXiv:2106.11853</i>, 2021.","ieee":"J. Lienen and E. Hüllermeier, “Credal Self-Supervised Learning,” <i>arXiv:2106.11853</i>. 2021."},"year":"2021","user_id":"44040","_id":"22509","language":[{"iso":"eng"}],"publication":"arXiv:2106.11853","type":"preprint","status":"public","abstract":[{"text":"Self-training is an effective approach to semi-supervised learning. The key idea is to let the learner itself iteratively generate \"pseudo-supervision\" for unlabeled instances based on its current hypothesis. In combination with consistency regularization, pseudo-labeling has shown promising performance in various domains, for example in computer vision. To account for the hypothetical nature of the pseudo-labels, these are commonly provided in the form of probability distributions. Still, one may argue that even a probability distribution represents an excessive level of informedness, as it suggests that the learner precisely knows the ground-truth conditional probabilities. In our approach, we therefore allow the learner to label instances in the form of credal sets, that is, sets of (candidate) probability distributions. Thanks to this increased expressiveness, the learner is able to represent uncertainty and a lack of knowledge in a more flexible and more faithful manner. To learn from weakly labeled data of that kind, we leverage methods that have recently been proposed in the realm of so-called superset learning. In an exhaustive empirical evaluation, we compare our methodology to state-of-the-art self-supervision approaches, showing competitive to superior performance especially in low-label scenarios incorporating a high degree of uncertainty.","lang":"eng"}]},{"quality_controlled":"1","year":"2021","citation":{"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} }","short":"E. Hüllermeier, F. Mohr, A. Tornede, M.D. Wever, in: 2021.","mla":"Hüllermeier, Eyke, et al. <i>Automated Machine Learning, Bounded Rationality, and Rational Metareasoning</i>. 2021.","apa":"Hüllermeier, E., Mohr, F., Tornede, A., &#38; Wever, M. D. (2021). <i>Automated Machine Learning, Bounded Rationality, and Rational Metareasoning</i>. ECML/PKDD Workshop on Automating Data Science, Bilbao (Virtual).","ieee":"E. Hüllermeier, F. Mohr, A. Tornede, and M. D. Wever, “Automated Machine Learning, Bounded Rationality, and Rational Metareasoning,” presented at the ECML/PKDD Workshop on Automating Data Science, Bilbao (Virtual), 2021.","chicago":"Hüllermeier, Eyke, Felix Mohr, Alexander Tornede, and Marcel Dominik Wever. “Automated Machine Learning, Bounded Rationality, and Rational Metareasoning,” 2021.","ama":"Hüllermeier E, Mohr F, Tornede A, Wever MD. Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. In: ; 2021."},"date_updated":"2022-01-06T06:55:43Z","date_created":"2021-08-02T07:46:29Z","author":[{"last_name":"Hüllermeier","id":"48129","full_name":"Hüllermeier, Eyke","first_name":"Eyke"},{"full_name":"Mohr, Felix","last_name":"Mohr","first_name":"Felix"},{"last_name":"Tornede","full_name":"Tornede, Alexander","id":"38209","first_name":"Alexander"},{"first_name":"Marcel Dominik","full_name":"Wever, Marcel Dominik","id":"33176","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818"}],"title":"Automated Machine Learning, Bounded Rationality, and Rational Metareasoning","conference":{"name":"ECML/PKDD Workshop on Automating Data Science","start_date":"2021-09-13","end_date":"2021-09-17","location":"Bilbao (Virtual)"},"type":"conference","status":"public","_id":"22913","project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Project Area B","_id":"3"},{"_id":"10","name":"SFB 901 - Subproject B2"}],"department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"user_id":"5786","language":[{"iso":"eng"}]},{"conference":{"location":"Halifax, Canada","end_date":"2021-10-13","start_date":"2021-10-11","name":"24th International Conference on Discovery Science"},"doi":"10.1007/978-3-030-88942-5","author":[{"full_name":"Damke, Clemens","id":"48192","orcid":"0000-0002-0455-0048","last_name":"Damke","first_name":"Clemens"},{"first_name":"Eyke","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","id":"48129"}],"volume":12986,"date_updated":"2022-04-11T22:08:12Z","citation":{"ama":"Damke C, Hüllermeier E. Ranking Structured Objects with Graph Neural Networks. In: Soares C, Torgo L, eds. <i>Proceedings of The 24th International Conference on Discovery Science (DS 2021)</i>. Vol 12986. Lecture Notes in Computer Science. Springer; 2021:166-180. doi:<a href=\"https://doi.org/10.1007/978-3-030-88942-5\">10.1007/978-3-030-88942-5</a>","ieee":"C. Damke and E. Hüllermeier, “Ranking Structured Objects with Graph Neural Networks,” in <i>Proceedings of The 24th International Conference on Discovery Science (DS 2021)</i>, Halifax, Canada, 2021, vol. 12986, pp. 166–180, doi: <a href=\"https://doi.org/10.1007/978-3-030-88942-5\">10.1007/978-3-030-88942-5</a>.","chicago":"Damke, Clemens, and Eyke Hüllermeier. “Ranking Structured Objects with Graph Neural Networks.” In <i>Proceedings of The 24th International Conference on Discovery Science (DS 2021)</i>, edited by Carlos Soares and Luis Torgo, 12986:166–80. Lecture Notes in Computer Science. Springer, 2021. <a href=\"https://doi.org/10.1007/978-3-030-88942-5\">https://doi.org/10.1007/978-3-030-88942-5</a>.","apa":"Damke, C., &#38; Hüllermeier, E. (2021). Ranking Structured Objects with Graph Neural Networks. In C. Soares &#38; L. Torgo (Eds.), <i>Proceedings of The 24th International Conference on Discovery Science (DS 2021)</i> (Vol. 12986, pp. 166–180). Springer. <a href=\"https://doi.org/10.1007/978-3-030-88942-5\">https://doi.org/10.1007/978-3-030-88942-5</a>","bibtex":"@inproceedings{Damke_Hüllermeier_2021, series={Lecture Notes in Computer Science}, title={Ranking Structured Objects with Graph Neural Networks}, volume={12986}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-88942-5\">10.1007/978-3-030-88942-5</a>}, booktitle={Proceedings of The 24th International Conference on Discovery Science (DS 2021)}, publisher={Springer}, author={Damke, Clemens and Hüllermeier, Eyke}, editor={Soares, Carlos and Torgo, Luis}, year={2021}, pages={166–180}, collection={Lecture Notes in Computer Science} }","mla":"Damke, Clemens, and Eyke Hüllermeier. “Ranking Structured Objects with Graph Neural Networks.” <i>Proceedings of The 24th International Conference on Discovery Science (DS 2021)</i>, edited by Carlos Soares and Luis Torgo, vol. 12986, Springer, 2021, pp. 166–80, doi:<a href=\"https://doi.org/10.1007/978-3-030-88942-5\">10.1007/978-3-030-88942-5</a>.","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."},"intvolume":"     12986","page":"166-180","publication_status":"published","publication_identifier":{"isbn":["9783030889418","9783030889425"],"issn":["0302-9743","1611-3349"]},"user_id":"48192","series_title":"Lecture Notes in Computer Science","department":[{"_id":"355"}],"_id":"27381","status":"public","editor":[{"full_name":"Soares, Carlos","last_name":"Soares","first_name":"Carlos"},{"full_name":"Torgo, Luis","last_name":"Torgo","first_name":"Luis"}],"type":"conference","title":"Ranking Structured Objects with Graph Neural Networks","date_created":"2021-11-11T14:15:18Z","publisher":"Springer","year":"2021","quality_controlled":"1","language":[{"iso":"eng"}],"keyword":["Graph-structured data","Graph neural networks","Preference learning","Learning to rank"],"external_id":{"arxiv":["2104.08869"]},"abstract":[{"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.","lang":"eng"}],"publication":"Proceedings of The 24th International Conference on Discovery Science (DS 2021)"},{"language":[{"iso":"eng"}],"_id":"30866","external_id":{"arxiv":["2111.05850"]},"project":[{"name":"SFB 901: SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - B: SFB 901 - Project Area B"},{"name":"SFB 901 - B2: SFB 901 - Subproject B2","_id":"10"}],"department":[{"_id":"34"},{"_id":"7"},{"_id":"26"}],"user_id":"38209","abstract":[{"lang":"eng","text":"Automated machine learning (AutoML) strives for the automatic configuration\r\nof machine learning algorithms and their composition into an overall (software)\r\nsolution - a machine learning pipeline - tailored to the learning task\r\n(dataset) at hand. Over the last decade, AutoML has developed into an\r\nindependent research field with hundreds of contributions. While AutoML offers\r\nmany prospects, it is also known to be quite resource-intensive, which is one\r\nof its major points of criticism. The primary cause for a high resource\r\nconsumption is that many approaches rely on the (costly) evaluation of many\r\nmachine learning pipelines while searching for good candidates. This problem is\r\namplified in the context of research on AutoML methods, due to large scale\r\nexperiments conducted with many datasets and approaches, each of them being run\r\nwith several repetitions to rule out random effects. In the spirit of recent\r\nwork on Green AI, this paper is written in an attempt to raise the awareness of\r\nAutoML researchers for the problem and to elaborate on possible remedies. To\r\nthis end, we identify four categories of actions the community may take towards\r\nmore sustainable research on AutoML, i.e. Green AutoML: design of AutoML\r\nsystems, benchmarking, transparency and research incentives."}],"status":"public","publication":"arXiv:2111.05850","type":"preprint","title":"Towards Green Automated Machine Learning: Status Quo and Future Directions","date_updated":"2022-04-12T12:01:23Z","date_created":"2022-04-12T11:57:15Z","author":[{"first_name":"Tanja","last_name":"Tornede","full_name":"Tornede, Tanja","id":"40795"},{"first_name":"Alexander","full_name":"Tornede, Alexander","id":"38209","last_name":"Tornede"},{"full_name":"Hanselle, Jonas Manuel","id":"43980","last_name":"Hanselle","orcid":"0000-0002-1231-4985","first_name":"Jonas Manuel"},{"first_name":"Marcel Dominik","full_name":"Wever, Marcel Dominik","id":"33176","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818"},{"full_name":"Mohr, Felix","last_name":"Mohr","first_name":"Felix"},{"first_name":"Eyke","id":"48129","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier"}],"year":"2021","citation":{"apa":"Tornede, T., Tornede, A., Hanselle, J. M., Wever, M. D., Mohr, F., &#38; Hüllermeier, E. (2021). Towards Green Automated Machine Learning: Status Quo and Future Directions. In <i>arXiv:2111.05850</i>.","short":"T. Tornede, A. Tornede, J.M. Hanselle, M.D. Wever, F. Mohr, E. Hüllermeier, ArXiv:2111.05850 (2021).","mla":"Tornede, Tanja, et al. “Towards Green Automated Machine Learning: Status Quo and Future Directions.” <i>ArXiv:2111.05850</i>, 2021.","bibtex":"@article{Tornede_Tornede_Hanselle_Wever_Mohr_Hüllermeier_2021, title={Towards Green Automated Machine Learning: Status Quo and Future Directions}, journal={arXiv:2111.05850}, author={Tornede, Tanja and Tornede, Alexander and Hanselle, Jonas Manuel and Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2021} }","ama":"Tornede T, Tornede A, Hanselle JM, Wever MD, Mohr F, Hüllermeier E. Towards Green Automated Machine Learning: Status Quo and Future Directions. <i>arXiv:211105850</i>. Published online 2021.","ieee":"T. Tornede, A. Tornede, J. M. Hanselle, M. D. Wever, F. Mohr, and E. Hüllermeier, “Towards Green Automated Machine Learning: Status Quo and Future Directions,” <i>arXiv:2111.05850</i>. 2021.","chicago":"Tornede, Tanja, Alexander Tornede, Jonas Manuel Hanselle, Marcel Dominik Wever, Felix Mohr, and Eyke Hüllermeier. “Towards Green Automated Machine Learning: Status Quo and Future Directions.” <i>ArXiv:2111.05850</i>, 2021."}},{"citation":{"apa":"Hanselle, J. M., Tornede, A., Wever, M. D., &#38; Hüllermeier, E. (2021). <i>Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data</i>. The 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2021), Delhi, India.","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} }","mla":"Hanselle, Jonas Manuel, et al. <i>Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data</i>. 2021.","short":"J.M. Hanselle, A. Tornede, M.D. Wever, E. Hüllermeier, (2021).","ama":"Hanselle JM, Tornede A, Wever MD, Hüllermeier E. Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data. Published online 2021.","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.","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."},"year":"2021","author":[{"full_name":"Hanselle, Jonas Manuel","id":"43980","last_name":"Hanselle","orcid":"0000-0002-1231-4985","first_name":"Jonas Manuel"},{"full_name":"Tornede, Alexander","id":"38209","last_name":"Tornede","first_name":"Alexander"},{"first_name":"Marcel Dominik","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik","id":"33176"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","id":"48129","last_name":"Hüllermeier"}],"date_created":"2021-02-09T09:30:14Z","date_updated":"2022-08-24T12:49:06Z","conference":{"name":"The 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2021)","start_date":"2021-05-11","end_date":"2021-05-14","location":"Delhi, India"},"title":"Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data","type":"conference","status":"public","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"user_id":"38209","series_title":"PAKDD","_id":"21198","project":[{"_id":"1","name":"SFB 901"},{"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"}],"language":[{"iso":"eng"}]},{"date_created":"2022-01-12T10:27:23Z","author":[{"last_name":"Feldhans","full_name":"Feldhans, Robert","first_name":"Robert"},{"full_name":"Wilke, Adrian","id":"9101","last_name":"Wilke","orcid":"0000-0002-6575-807X","first_name":"Adrian"},{"first_name":"Stefan","last_name":"Heindorf","orcid":"0000-0002-4525-6865","full_name":"Heindorf, Stefan","id":"11871"},{"full_name":"Shaker, Mohammad Hossein","last_name":"Shaker","first_name":"Mohammad Hossein"},{"full_name":"Hammer, Barbara","last_name":"Hammer","first_name":"Barbara"},{"id":"65716","full_name":"Ngonga Ngomo, Axel-Cyrille","last_name":"Ngonga Ngomo","first_name":"Axel-Cyrille"},{"last_name":"Hüllermeier","id":"48129","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"date_updated":"2022-10-15T19:54:20Z","oa":"1","publisher":"Springer International Publishing","main_file_link":[{"open_access":"1","url":"https://papers.dice-research.org/2021/IDEAL2021_DriftDetectionEmbeddings/Drift-Detection-in-Text-Data-with-Document-Embeddings-public.pdf"}],"doi":"10.1007/978-3-030-91608-4_11","title":"Drift Detection in Text Data with Document Embeddings","related_material":{"link":[{"relation":"confirmation","url":"https://link.springer.com/chapter/10.1007/978-3-030-91608-4_11"}]},"publication_status":"published","publication_identifier":{"isbn":["9783030916077","9783030916084"],"issn":["0302-9743","1611-3349"]},"citation":{"ama":"Feldhans R, Wilke A, Heindorf S, et al. Drift Detection in Text Data with Document Embeddings. In: <i>Intelligent Data Engineering and Automated Learning – IDEAL 2021</i>. Springer International Publishing; 2021. doi:<a href=\"https://doi.org/10.1007/978-3-030-91608-4_11\">10.1007/978-3-030-91608-4_11</a>","chicago":"Feldhans, Robert, Adrian Wilke, Stefan Heindorf, Mohammad Hossein Shaker, Barbara Hammer, Axel-Cyrille Ngonga Ngomo, and Eyke Hüllermeier. “Drift Detection in Text Data with Document Embeddings.” In <i>Intelligent Data Engineering and Automated Learning – IDEAL 2021</i>. Cham: Springer International Publishing, 2021. <a href=\"https://doi.org/10.1007/978-3-030-91608-4_11\">https://doi.org/10.1007/978-3-030-91608-4_11</a>.","ieee":"R. Feldhans <i>et al.</i>, “Drift Detection in Text Data with Document Embeddings,” in <i>Intelligent Data Engineering and Automated Learning – IDEAL 2021</i>, Cham: Springer International Publishing, 2021.","mla":"Feldhans, Robert, et al. “Drift Detection in Text Data with Document Embeddings.” <i>Intelligent Data Engineering and Automated Learning – IDEAL 2021</i>, Springer International Publishing, 2021, doi:<a href=\"https://doi.org/10.1007/978-3-030-91608-4_11\">10.1007/978-3-030-91608-4_11</a>.","bibtex":"@inbook{Feldhans_Wilke_Heindorf_Shaker_Hammer_Ngonga Ngomo_Hüllermeier_2021, place={Cham}, title={Drift Detection in Text Data with Document Embeddings}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-91608-4_11\">10.1007/978-3-030-91608-4_11</a>}, booktitle={Intelligent Data Engineering and Automated Learning – IDEAL 2021}, publisher={Springer International Publishing}, author={Feldhans, Robert and Wilke, Adrian and Heindorf, Stefan and Shaker, Mohammad Hossein and Hammer, Barbara and Ngonga Ngomo, Axel-Cyrille and Hüllermeier, Eyke}, year={2021} }","short":"R. Feldhans, A. Wilke, S. Heindorf, M.H. Shaker, B. Hammer, A.-C. Ngonga Ngomo, E. Hüllermeier, in: Intelligent Data Engineering and Automated Learning – IDEAL 2021, Springer International Publishing, Cham, 2021.","apa":"Feldhans, R., Wilke, A., Heindorf, S., Shaker, M. H., Hammer, B., Ngonga Ngomo, A.-C., &#38; Hüllermeier, E. (2021). Drift Detection in Text Data with Document Embeddings. In <i>Intelligent Data Engineering and Automated Learning – IDEAL 2021</i>. Springer International Publishing. <a href=\"https://doi.org/10.1007/978-3-030-91608-4_11\">https://doi.org/10.1007/978-3-030-91608-4_11</a>"},"year":"2021","place":"Cham","user_id":"11871","department":[{"_id":"574"}],"_id":"29292","language":[{"iso":"eng"}],"type":"book_chapter","publication":"Intelligent Data Engineering and Automated Learning – IDEAL 2021","status":"public"},{"language":[{"iso":"eng"}],"ddc":["300"],"keyword":["Explainability","process ofexplaining andunderstanding","explainable artificial systems"],"file":[{"relation":"main_file","content_type":"application/pdf","file_name":"2020-12-01_explainability_final_version.pdf","file_id":"49081","access_level":"open_access","file_size":626217,"creator":"haebumb","date_created":"2023-11-20T16:33:51Z","date_updated":"2023-11-20T16:33:51Z"}],"abstract":[{"lang":"eng","text":"One objective of current research in explainable intelligent systems is to implement social aspects in order to increase the relevance of explanations. In this paper, we argue that a novel conceptual framework is needed to overcome shortcomings of existing AI systems with little attention to processes of interaction and learning. Drawing from research in interaction and development, we first outline the novel conceptual framework that pushes the design of AI systems toward true interactivity with an emphasis on the role of the partner and social relevance. We propose that AI systems will be able to provide a meaningful and relevant explanation only if the process of explaining is extended to active contribution of both partners that brings about dynamics that is modulated by different levels of analysis. Accordingly, our conceptual framework comprises monitoring and scaffolding as key concepts and claims that the process of explaining is not only modulated by the interaction between explainee and explainer but is embedded into a larger social context in which conventionalized and routinized behaviors are established. We discuss our conceptual framework in relation to the established objectives of transparency and autonomy that are raised for the design of explainable AI systems currently."}],"publication":"IEEE Transactions on Cognitive and Developmental Systems","title":"Explanation as a Social Practice: Toward a Conceptual Framework for the Social Design of AI Systems","date_created":"2021-09-14T20:52:57Z","year":"2021","issue":"3","quality_controlled":"1","file_date_updated":"2023-11-20T16:33:51Z","article_type":"original","user_id":"42933","department":[{"_id":"603"},{"_id":"749"},{"_id":"424"},{"_id":"67"},{"_id":"574"},{"_id":"184"},{"_id":"757"},{"_id":"54"},{"_id":"178"}],"project":[{"name":"TRR 318: TRR 318 - Erklärbarkeit konstruieren","_id":"109","grant_number":"438445824"}],"_id":"24456","status":"public","type":"journal_article","doi":"10.1109/tcds.2020.3044366","author":[{"last_name":"Rohlfing","id":"50352","full_name":"Rohlfing, Katharina J.","first_name":"Katharina J."},{"last_name":"Cimiano","full_name":"Cimiano, Philipp","first_name":"Philipp"},{"last_name":"Scharlau","orcid":"0000-0003-2364-9489","full_name":"Scharlau, Ingrid","id":"451","first_name":"Ingrid"},{"last_name":"Matzner","id":"65695","full_name":"Matzner, Tobias","first_name":"Tobias"},{"full_name":"Buhl, Heike M.","id":"27152","last_name":"Buhl","first_name":"Heike M."},{"first_name":"Hendrik","full_name":"Buschmeier, Hendrik","last_name":"Buschmeier"},{"last_name":"Esposito","full_name":"Esposito, Elena","first_name":"Elena"},{"id":"57578","full_name":"Grimminger, Angela","last_name":"Grimminger","first_name":"Angela"},{"full_name":"Hammer, Barbara","last_name":"Hammer","first_name":"Barbara"},{"first_name":"Reinhold","id":"242","full_name":"Haeb-Umbach, Reinhold","last_name":"Haeb-Umbach"},{"last_name":"Horwath","id":"68836","full_name":"Horwath, Ilona","first_name":"Ilona"},{"last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","id":"48129","first_name":"Eyke"},{"last_name":"Kern","full_name":"Kern, Friederike","first_name":"Friederike"},{"last_name":"Kopp","full_name":"Kopp, Stefan","first_name":"Stefan"},{"first_name":"Kirsten","full_name":"Thommes, Kirsten","id":"72497","last_name":"Thommes"},{"first_name":"Axel-Cyrille","last_name":"Ngonga Ngomo","full_name":"Ngonga Ngomo, Axel-Cyrille","id":"65716"},{"first_name":"Carsten","full_name":"Schulte, Carsten","id":"60311","last_name":"Schulte"},{"full_name":"Wachsmuth, Henning","id":"3900","last_name":"Wachsmuth","first_name":"Henning"},{"first_name":"Petra","last_name":"Wagner","full_name":"Wagner, Petra"},{"full_name":"Wrede, Britta","last_name":"Wrede","first_name":"Britta"}],"volume":13,"oa":"1","date_updated":"2023-12-05T10:15:02Z","citation":{"apa":"Rohlfing, K. J., Cimiano, P., Scharlau, I., Matzner, T., Buhl, H. M., Buschmeier, H., Esposito, E., Grimminger, A., Hammer, B., Haeb-Umbach, R., Horwath, I., Hüllermeier, E., Kern, F., Kopp, S., Thommes, K., Ngonga Ngomo, A.-C., Schulte, C., Wachsmuth, H., Wagner, P., &#38; Wrede, B. (2021). Explanation as a Social Practice: Toward a Conceptual Framework for the Social Design of AI Systems. <i>IEEE Transactions on Cognitive and Developmental Systems</i>, <i>13</i>(3), 717–728. <a href=\"https://doi.org/10.1109/tcds.2020.3044366\">https://doi.org/10.1109/tcds.2020.3044366</a>","mla":"Rohlfing, Katharina J., et al. “Explanation as a Social Practice: Toward a Conceptual Framework for the Social Design of AI Systems.” <i>IEEE Transactions on Cognitive and Developmental Systems</i>, vol. 13, no. 3, 2021, pp. 717–28, doi:<a href=\"https://doi.org/10.1109/tcds.2020.3044366\">10.1109/tcds.2020.3044366</a>.","short":"K.J. Rohlfing, P. Cimiano, I. Scharlau, T. Matzner, H.M. Buhl, H. Buschmeier, E. Esposito, A. Grimminger, B. Hammer, R. Haeb-Umbach, I. Horwath, E. Hüllermeier, F. Kern, S. Kopp, K. Thommes, A.-C. Ngonga Ngomo, C. Schulte, H. Wachsmuth, P. Wagner, B. Wrede, IEEE Transactions on Cognitive and Developmental Systems 13 (2021) 717–728.","bibtex":"@article{Rohlfing_Cimiano_Scharlau_Matzner_Buhl_Buschmeier_Esposito_Grimminger_Hammer_Haeb-Umbach_et al._2021, title={Explanation as a Social Practice: Toward a Conceptual Framework for the Social Design of AI Systems}, volume={13}, DOI={<a href=\"https://doi.org/10.1109/tcds.2020.3044366\">10.1109/tcds.2020.3044366</a>}, number={3}, journal={IEEE Transactions on Cognitive and Developmental Systems}, author={Rohlfing, Katharina J. and Cimiano, Philipp and Scharlau, Ingrid and Matzner, Tobias and Buhl, Heike M. and Buschmeier, Hendrik and Esposito, Elena and Grimminger, Angela and Hammer, Barbara and Haeb-Umbach, Reinhold and et al.}, year={2021}, pages={717–728} }","ieee":"K. J. Rohlfing <i>et al.</i>, “Explanation as a Social Practice: Toward a Conceptual Framework for the Social Design of AI Systems,” <i>IEEE Transactions on Cognitive and Developmental Systems</i>, vol. 13, no. 3, pp. 717–728, 2021, doi: <a href=\"https://doi.org/10.1109/tcds.2020.3044366\">10.1109/tcds.2020.3044366</a>.","chicago":"Rohlfing, Katharina J., Philipp Cimiano, Ingrid Scharlau, Tobias Matzner, Heike M. Buhl, Hendrik Buschmeier, Elena Esposito, et al. “Explanation as a Social Practice: Toward a Conceptual Framework for the Social Design of AI Systems.” <i>IEEE Transactions on Cognitive and Developmental Systems</i> 13, no. 3 (2021): 717–28. <a href=\"https://doi.org/10.1109/tcds.2020.3044366\">https://doi.org/10.1109/tcds.2020.3044366</a>.","ama":"Rohlfing KJ, Cimiano P, Scharlau I, et al. Explanation as a Social Practice: Toward a Conceptual Framework for the Social Design of AI Systems. <i>IEEE Transactions on Cognitive and Developmental Systems</i>. 2021;13(3):717-728. doi:<a href=\"https://doi.org/10.1109/tcds.2020.3044366\">10.1109/tcds.2020.3044366</a>"},"page":"717-728","intvolume":"        13","publication_status":"published","has_accepted_license":"1","publication_identifier":{"issn":["2379-8920","2379-8939"]}},{"abstract":[{"text":"Aggregation metrics in reputation systems are important for overcoming information overload. When using these metrics, technical aggregation functions such as the arithmetic mean are implemented to measure the valence of product ratings. However, it is unclear whether the implemented aggregation functions match the inherent aggregation patterns of customers. In our experiment, we elicit customers' aggregation heuristics and contrast these with reference functions. Our findings indicate that, overall, the arithmetic mean performs best in comparison with other aggregation functions. However, our analysis on an individual level reveals heterogeneous aggregation patterns. Major clusters exhibit a binary bias (i.e., an over-weighting of moderate ratings and under-weighting of extreme ratings) in combination with the arithmetic mean. Minor clusters focus on 1-star ratings or negative (i.e., 1-star and 2-star) ratings. Thereby, inherent aggregation patterns are neither affected by variation of provided information nor by individual characteristics such as experience, risk attitudes, or demographics.","lang":"eng"}],"status":"public","type":"working_paper","language":[{"iso":"eng"}],"project":[{"grant_number":"160364472","name":"SFB 901 - A4: SFB 901 - Empirische Analysen in Märkten für OTF Dienstleistungen (Subproject A4)","_id":"8"},{"grant_number":"160364472","name":"SFB 901: SFB 901: On-The-Fly Computing - Individualisierte IT-Dienstleistungen in dynamischen Märkten ","_id":"1"},{"_id":"2","name":"SFB 901 - A: SFB 901 - Project Area A"}],"_id":"45616","user_id":"477","series_title":"Working Papers Dissertations","year":"2021","citation":{"chicago":"Straaten, Dirk van, Vitalik Melnikov, Eyke Hüllermeier, Behnud Mir Djawadi, and René Fahr. <i>Accounting for Heuristics in Reputation Systems: An Interdisciplinary Approach on Aggregation Processes</i>. Vol. 72. Working Papers Dissertations, 2021.","ieee":"D. van Straaten, V. Melnikov, E. Hüllermeier, B. Mir Djawadi, and R. Fahr, <i>Accounting for Heuristics in Reputation Systems: An Interdisciplinary Approach on Aggregation Processes</i>, vol. 72. 2021.","ama":"van Straaten D, Melnikov V, Hüllermeier E, Mir Djawadi B, Fahr R. <i>Accounting for Heuristics in Reputation Systems: An Interdisciplinary Approach on Aggregation Processes</i>. Vol 72.; 2021.","apa":"van Straaten, D., Melnikov, V., Hüllermeier, E., Mir Djawadi, B., &#38; Fahr, R. (2021). <i>Accounting for Heuristics in Reputation Systems: An Interdisciplinary Approach on Aggregation Processes</i> (Vol. 72).","short":"D. van Straaten, V. Melnikov, E. Hüllermeier, B. Mir Djawadi, R. Fahr, Accounting for Heuristics in Reputation Systems: An Interdisciplinary Approach on Aggregation Processes, 2021.","bibtex":"@book{van Straaten_Melnikov_Hüllermeier_Mir Djawadi_Fahr_2021, series={Working Papers Dissertations}, title={Accounting for Heuristics in Reputation Systems: An Interdisciplinary Approach on Aggregation Processes}, volume={72}, author={van Straaten, Dirk and Melnikov, Vitalik and Hüllermeier, Eyke and Mir Djawadi, Behnud and Fahr, René}, year={2021}, collection={Working Papers Dissertations} }","mla":"van Straaten, Dirk, et al. <i>Accounting for Heuristics in Reputation Systems: An Interdisciplinary Approach on Aggregation Processes</i>. 2021."},"intvolume":"        72","title":"Accounting for Heuristics in Reputation Systems: An Interdisciplinary Approach on Aggregation Processes","date_updated":"2023-07-05T07:27:17Z","author":[{"id":"10311","full_name":"van Straaten, Dirk","last_name":"van Straaten","first_name":"Dirk"},{"last_name":"Melnikov","full_name":"Melnikov, Vitalik","id":"58747","first_name":"Vitalik"},{"first_name":"Eyke","id":"48129","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier"},{"id":"26032","full_name":"Mir Djawadi, Behnud","orcid":"0000-0002-6271-5912","last_name":"Mir Djawadi","first_name":"Behnud"},{"first_name":"René","full_name":"Fahr, René","id":"111","last_name":"Fahr"}],"date_created":"2023-06-15T08:23:33Z","volume":72},{"language":[{"iso":"eng"}],"_id":"19603","user_id":"39640","abstract":[{"lang":"eng","text":"Micro- and smart grids (MSG) play an important role both for integrating\r\nrenewable energy sources in conventional electricity grids and for providing\r\npower supply in remote areas. Modern MSGs are largely driven by power\r\nelectronic converters due to their high efficiency and flexibility.\r\nNevertheless, controlling MSGs is a challenging task due to highest\r\nrequirements on energy availability, safety and voltage quality within a wide\r\nrange of different MSG topologies. This results in a high demand for\r\ncomprehensive testing of new control concepts during their development phase\r\nand comparisons with the state of the art in order to ensure their feasibility.\r\nThis applies in particular to data-driven control approaches from the field of\r\nreinforcement learning (RL), whose stability and operating behavior can hardly\r\nbe evaluated a priori. Therefore, the OpenModelica Microgrid Gym (OMG) package,\r\nan open-source software toolbox for the simulation and control optimization of\r\nMSGs, is proposed. It is capable of modeling and simulating arbitrary MSG\r\ntopologies and offers a Python-based interface for plug \\& play controller\r\ntesting. In particular, the standardized OpenAI Gym interface allows for easy\r\nRL-based controller integration. Besides the presentation of the OMG toolbox,\r\napplication examples are highlighted including safe Bayesian optimization for\r\nlow-level controller tuning."}],"status":"public","publication":"arXiv:2005.04869","type":"preprint","title":"Towards a Scalable and Flexible Simulation and Testing Environment  Toolbox for Intelligent Microgrid Control","main_file_link":[{"open_access":"1","url":"https://arxiv.org/pdf/2005.04869.pdf"}],"date_updated":"2022-01-06T06:54:07Z","oa":"1","author":[{"first_name":"Henrik","last_name":"Bode","full_name":"Bode, Henrik"},{"first_name":"Stefan Helmut","id":"39640","full_name":"Heid, Stefan Helmut","last_name":"Heid","orcid":"0000-0002-9461-7372"},{"first_name":"Daniel","full_name":"Weber, Daniel","last_name":"Weber"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","id":"48129","last_name":"Hüllermeier"},{"full_name":"Wallscheid, Oliver","last_name":"Wallscheid","first_name":"Oliver"}],"date_created":"2020-09-21T10:01:36Z","year":"2020","citation":{"apa":"Bode, H., Heid, S. H., Weber, D., Hüllermeier, E., &#38; Wallscheid, O. (2020). Towards a Scalable and Flexible Simulation and Testing Environment  Toolbox for Intelligent Microgrid Control. <i>ArXiv:2005.04869</i>.","bibtex":"@article{Bode_Heid_Weber_Hüllermeier_Wallscheid_2020, title={Towards a Scalable and Flexible Simulation and Testing Environment  Toolbox for Intelligent Microgrid Control}, journal={arXiv:2005.04869}, author={Bode, Henrik and Heid, Stefan Helmut and Weber, Daniel and Hüllermeier, Eyke and Wallscheid, Oliver}, year={2020} }","short":"H. Bode, S.H. Heid, D. Weber, E. Hüllermeier, O. Wallscheid, ArXiv:2005.04869 (2020).","mla":"Bode, Henrik, et al. “Towards a Scalable and Flexible Simulation and Testing Environment  Toolbox for Intelligent Microgrid Control.” <i>ArXiv:2005.04869</i>, 2020.","ieee":"H. Bode, S. H. Heid, D. Weber, E. Hüllermeier, and O. Wallscheid, “Towards a Scalable and Flexible Simulation and Testing Environment  Toolbox for Intelligent Microgrid Control,” <i>arXiv:2005.04869</i>. 2020.","chicago":"Bode, Henrik, Stefan Helmut Heid, Daniel Weber, Eyke Hüllermeier, and Oliver Wallscheid. “Towards a Scalable and Flexible Simulation and Testing Environment  Toolbox for Intelligent Microgrid Control.” <i>ArXiv:2005.04869</i>, 2020.","ama":"Bode H, Heid SH, Weber D, Hüllermeier E, Wallscheid O. Towards a Scalable and Flexible Simulation and Testing Environment  Toolbox for Intelligent Microgrid Control. <i>arXiv:200504869</i>. 2020."}},{"title":"A Novel Higher-order Weisfeiler-Lehman Graph Convolution","date_created":"2020-10-08T10:48:38Z","publisher":"PMLR","year":"2020","quality_controlled":"1","language":[{"iso":"eng"}],"ddc":["006"],"keyword":["graph neural networks","Weisfeiler-Lehman test","cycle detection"],"external_id":{"arxiv":["2007.00346"]},"file":[{"relation":"main_file","content_type":"application/pdf","file_size":771137,"file_id":"19954","file_name":"damke20.pdf","access_level":"open_access","date_updated":"2020-10-08T11:21:00Z","date_created":"2020-10-08T10:54:48Z","creator":"cdamke"},{"file_size":613163,"access_level":"open_access","file_id":"19955","file_name":"damke20-supp.pdf","date_updated":"2020-10-08T11:24:29Z","date_created":"2020-10-08T10:54:59Z","creator":"cdamke","relation":"supplementary_material","content_type":"application/pdf"}],"abstract":[{"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.","lang":"eng"}],"publication":"Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020)","conference":{"end_date":"2020-11-20","location":"Bangkok, Thailand","name":"Asian Conference on Machine Learning","start_date":"2020-11-18"},"author":[{"first_name":"Clemens","last_name":"Damke","orcid":"0000-0002-0455-0048","full_name":"Damke, Clemens","id":"48192"},{"full_name":"Melnikov, Vitaly","id":"58747","last_name":"Melnikov","first_name":"Vitaly"},{"last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","id":"48129","first_name":"Eyke"}],"volume":129,"date_updated":"2022-01-06T06:54:17Z","oa":"1","citation":{"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.","mla":"Damke, Clemens, et al. “A Novel Higher-Order Weisfeiler-Lehman Graph Convolution.” <i>Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020)</i>, edited by Sinno Jialin Pan and Masashi Sugiyama, vol. 129, PMLR, 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} }","apa":"Damke, C., Melnikov, V., &#38; Hüllermeier, E. (2020). A Novel Higher-order Weisfeiler-Lehman Graph Convolution. In S. Jialin Pan &#38; M. Sugiyama (Eds.), <i>Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020)</i> (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. <i>Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020)</i>. Vol 129. Proceedings of Machine Learning Research. Bangkok, Thailand: PMLR; 2020:49-64.","ieee":"C. Damke, V. Melnikov, and E. Hüllermeier, “A Novel Higher-order Weisfeiler-Lehman Graph Convolution,” in <i>Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020)</i>, Bangkok, Thailand, 2020, vol. 129, pp. 49–64.","chicago":"Damke, Clemens, Vitaly Melnikov, and Eyke Hüllermeier. “A Novel Higher-Order Weisfeiler-Lehman Graph Convolution.” In <i>Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020)</i>, edited by Sinno Jialin Pan and Masashi Sugiyama, 129:49–64. Proceedings of Machine Learning Research. Bangkok, Thailand: PMLR, 2020."},"page":"49-64","intvolume":"       129","place":"Bangkok, Thailand","publication_status":"published","has_accepted_license":"1","file_date_updated":"2020-10-08T11:24:29Z","user_id":"48192","series_title":"Proceedings of Machine Learning Research","department":[{"_id":"355"}],"_id":"19953","status":"public","editor":[{"first_name":"Sinno","full_name":"Jialin Pan, Sinno","last_name":"Jialin Pan"},{"first_name":"Masashi","full_name":"Sugiyama, Masashi","last_name":"Sugiyama"}],"type":"conference"},{"year":"2020","citation":{"ama":"Lienen J, Hüllermeier E. Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce  model. <i>arXiv:201013118</i>. 2020.","ieee":"J. Lienen and E. Hüllermeier, “Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce  model,” <i>arXiv:2010.13118</i>. 2020.","chicago":"Lienen, Julian, and Eyke Hüllermeier. “Monocular Depth Estimation via Listwise Ranking Using the Plackett-Luce  Model.” <i>ArXiv:2010.13118</i>, 2020.","apa":"Lienen, J., &#38; Hüllermeier, E. (2020). Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce  model. <i>ArXiv:2010.13118</i>.","bibtex":"@article{Lienen_Hüllermeier_2020, title={Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce  model}, journal={arXiv:2010.13118}, author={Lienen, Julian and Hüllermeier, Eyke}, year={2020} }","mla":"Lienen, Julian, and Eyke Hüllermeier. “Monocular Depth Estimation via Listwise Ranking Using the Plackett-Luce  Model.” <i>ArXiv:2010.13118</i>, 2020.","short":"J. Lienen, E. Hüllermeier, ArXiv:2010.13118 (2020)."},"oa":"1","date_updated":"2022-01-06T06:54:23Z","date_created":"2020-10-27T07:48:40Z","author":[{"first_name":"Julian","id":"44040","full_name":"Lienen, Julian","last_name":"Lienen"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","id":"48129","last_name":"Hüllermeier"}],"title":"Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce  model","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2010.13118"}],"type":"preprint","publication":"arXiv:2010.13118","abstract":[{"lang":"eng","text":"In many real-world applications, the relative depth of objects in an image is\r\ncrucial for scene understanding, e.g., to calculate occlusions in augmented\r\nreality scenes. Predicting depth in monocular images has recently been tackled\r\nusing machine learning methods, mainly by treating the problem as a regression\r\ntask. Yet, being interested in an order relation in the first place,\r\nranking methods suggest themselves as a natural alternative to regression, and\r\nindeed, ranking approaches leveraging pairwise comparisons as training\r\ninformation (\"object A is closer to the camera than B\") have shown promising\r\nperformance on this problem. In this paper, we elaborate on the use of\r\nso-called \\emph{listwise} ranking as a generalization of the pairwise approach.\r\nListwise ranking goes beyond pairwise comparisons between objects and considers\r\nrankings of arbitrary length as training information. Our approach is based on\r\nthe Plackett-Luce model, a probability distribution on rankings, which we\r\ncombine with a state-of-the-art neural network architecture and a sampling\r\nstrategy to reduce training complexity. An empirical evaluation on benchmark\r\ndata in a \"zero-shot\" setting demonstrates the effectiveness of our proposal\r\ncompared to existing ranking and regression methods."}],"status":"public","_id":"20211","user_id":"44040","language":[{"iso":"eng"}]},{"publication":"Proceedings-30. Workshop Computational Intelligence: Berlin, 26.-27. November 2020","type":"conference","status":"public","user_id":"66937","_id":"24146","language":[{"iso":"eng"}],"page":"247","intvolume":"        26","citation":{"chicago":"Heid, Stefan Helmut, Arunselvan Ramaswamy, and Eyke Hüllermeier. “Constrained Multi-Agent Optimization with Unbounded Information Delay.” In <i>Proceedings-30. Workshop Computational Intelligence: Berlin, 26.-27. November 2020</i>, 26:247, 2020.","ieee":"S. H. Heid, A. Ramaswamy, and E. Hüllermeier, “Constrained Multi-Agent Optimization with Unbounded Information Delay,” in <i>Proceedings-30. Workshop Computational Intelligence: Berlin, 26.-27. November 2020</i>, 2020, vol. 26, p. 247.","ama":"Heid SH, Ramaswamy A, Hüllermeier E. Constrained Multi-Agent Optimization with Unbounded Information Delay. In: <i>Proceedings-30. Workshop Computational Intelligence: Berlin, 26.-27. November 2020</i>. Vol 26. ; 2020:247.","apa":"Heid, S. H., Ramaswamy, A., &#38; Hüllermeier, E. (2020). Constrained Multi-Agent Optimization with Unbounded Information Delay. <i>Proceedings-30. Workshop Computational Intelligence: Berlin, 26.-27. November 2020</i>, <i>26</i>, 247.","mla":"Heid, Stefan Helmut, et al. “Constrained Multi-Agent Optimization with Unbounded Information Delay.” <i>Proceedings-30. Workshop Computational Intelligence: Berlin, 26.-27. November 2020</i>, vol. 26, 2020, p. 247.","bibtex":"@inproceedings{Heid_Ramaswamy_Hüllermeier_2020, title={Constrained Multi-Agent Optimization with Unbounded Information Delay}, volume={26}, booktitle={Proceedings-30. Workshop Computational Intelligence: Berlin, 26.-27. November 2020}, author={Heid, Stefan Helmut and Ramaswamy, Arunselvan and Hüllermeier, Eyke}, year={2020}, pages={247} }","short":"S.H. Heid, A. Ramaswamy, E. Hüllermeier, in: Proceedings-30. Workshop Computational Intelligence: Berlin, 26.-27. November 2020, 2020, p. 247."},"year":"2020","volume":26,"author":[{"first_name":"Stefan Helmut","full_name":"Heid, Stefan Helmut","id":"39640","last_name":"Heid","orcid":"0000-0002-9461-7372"},{"orcid":"https://orcid.org/ 0000-0001-7547-8111","last_name":"Ramaswamy","full_name":"Ramaswamy, Arunselvan","id":"66937","first_name":"Arunselvan"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","id":"48129","last_name":"Hüllermeier"}],"date_created":"2021-09-10T09:59:16Z","date_updated":"2022-01-06T06:56:08Z","title":"Constrained Multi-Agent Optimization with Unbounded Information Delay"},{"status":"public","publication":"Discovery Science","type":"conference","language":[{"iso":"eng"}],"department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"user_id":"5786","_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"}],"citation":{"ama":"Tornede A, Wever MD, Hüllermeier E. Extreme Algorithm Selection with Dyadic Feature Representation. In: <i>Discovery Science</i>. ; 2020.","chicago":"Tornede, Alexander, Marcel Dominik Wever, and Eyke Hüllermeier. “Extreme Algorithm Selection with Dyadic Feature Representation.” In <i>Discovery Science</i>, 2020.","ieee":"A. Tornede, M. D. Wever, and E. Hüllermeier, “Extreme Algorithm Selection with Dyadic Feature Representation,” presented at the Discovery Science 2020, 2020.","apa":"Tornede, A., Wever, M. D., &#38; Hüllermeier, E. (2020). Extreme Algorithm Selection with Dyadic Feature Representation. <i>Discovery Science</i>. Discovery Science 2020.","short":"A. Tornede, M.D. Wever, E. Hüllermeier, in: 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.” <i>Discovery Science</i>, 2020."},"year":"2020","conference":{"name":"Discovery Science 2020"},"title":"Extreme Algorithm Selection with Dyadic Feature Representation","author":[{"last_name":"Tornede","full_name":"Tornede, Alexander","id":"38209","first_name":"Alexander"},{"first_name":"Marcel Dominik","full_name":"Wever, Marcel Dominik","id":"33176","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","id":"48129","last_name":"Hüllermeier"}],"date_created":"2020-07-21T10:06:51Z","date_updated":"2022-01-06T06:53:10Z"},{"publication":"KI 2020: Advances in Artificial Intelligence","type":"conference","status":"public","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"user_id":"5786","_id":"17408","project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Project Area B","_id":"3"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"language":[{"iso":"eng"}],"citation":{"chicago":"Hanselle, Jonas Manuel, Alexander Tornede, Marcel Dominik Wever, and Eyke Hüllermeier. “Hybrid Ranking and Regression for Algorithm Selection.” In <i>KI 2020: Advances in Artificial Intelligence</i>, 2020.","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.","ama":"Hanselle JM, Tornede A, Wever MD, Hüllermeier E. Hybrid Ranking and Regression for Algorithm Selection. In: <i>KI 2020: Advances in Artificial Intelligence</i>. ; 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.” <i>KI 2020: Advances in Artificial Intelligence</i>, 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., &#38; Hüllermeier, E. (2020). Hybrid Ranking and Regression for Algorithm Selection. <i>KI 2020: Advances in Artificial Intelligence</i>. 43rd German Conference on Artificial Intelligence."},"year":"2020","date_created":"2020-07-21T10:21:09Z","author":[{"orcid":"0000-0002-1231-4985","last_name":"Hanselle","id":"43980","full_name":"Hanselle, Jonas Manuel","first_name":"Jonas Manuel"},{"first_name":"Alexander","id":"38209","full_name":"Tornede, Alexander","last_name":"Tornede"},{"full_name":"Wever, Marcel Dominik","id":"33176","orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","first_name":"Marcel Dominik"},{"first_name":"Eyke","last_name":"Hüllermeier","id":"48129","full_name":"Hüllermeier, Eyke"}],"date_updated":"2022-01-06T06:53:10Z","conference":{"name":"43rd German Conference on Artificial Intelligence"},"title":"Hybrid Ranking and Regression for Algorithm Selection"},{"language":[{"iso":"eng"}],"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"},{"name":"SFB 901","_id":"1"},{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"_id":"17424","user_id":"5786","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"status":"public","type":"conference","publication":"Proceedings of the ECMLPKDD 2020","title":"AutoML for Predictive Maintenance: One Tool to RUL Them All","doi":"10.1007/978-3-030-66770-2_8","conference":{"name":"IOTStream Workshop @ ECMLPKDD 2020"},"date_updated":"2022-01-06T06:53:11Z","date_created":"2020-07-28T09:17:41Z","author":[{"last_name":"Tornede","id":"40795","full_name":"Tornede, Tanja","first_name":"Tanja"},{"id":"38209","full_name":"Tornede, Alexander","last_name":"Tornede","first_name":"Alexander"},{"first_name":"Marcel Dominik","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818","id":"33176","full_name":"Wever, Marcel Dominik"},{"last_name":"Mohr","full_name":"Mohr, Felix","first_name":"Felix"},{"last_name":"Hüllermeier","id":"48129","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"year":"2020","citation":{"mla":"Tornede, Tanja, et al. “AutoML for Predictive Maintenance: One Tool to RUL Them All.” <i>Proceedings of the ECMLPKDD 2020</i>, 2020, doi:<a href=\"https://doi.org/10.1007/978-3-030-66770-2_8\">10.1007/978-3-030-66770-2_8</a>.","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={<a href=\"https://doi.org/10.1007/978-3-030-66770-2_8\">10.1007/978-3-030-66770-2_8</a>}, booktitle={Proceedings of the ECMLPKDD 2020}, author={Tornede, Tanja and Tornede, Alexander and Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2020} }","apa":"Tornede, T., Tornede, A., Wever, M. D., Mohr, F., &#38; Hüllermeier, E. (2020). AutoML for Predictive Maintenance: One Tool to RUL Them All. <i>Proceedings of the ECMLPKDD 2020</i>. IOTStream Workshop @ ECMLPKDD 2020. <a href=\"https://doi.org/10.1007/978-3-030-66770-2_8\">https://doi.org/10.1007/978-3-030-66770-2_8</a>","ama":"Tornede T, Tornede A, Wever MD, Mohr F, Hüllermeier E. AutoML for Predictive Maintenance: One Tool to RUL Them All. In: <i>Proceedings of the ECMLPKDD 2020</i>. ; 2020. doi:<a href=\"https://doi.org/10.1007/978-3-030-66770-2_8\">10.1007/978-3-030-66770-2_8</a>","chicago":"Tornede, Tanja, Alexander Tornede, Marcel Dominik Wever, Felix Mohr, and Eyke Hüllermeier. “AutoML for Predictive Maintenance: One Tool to RUL Them All.” In <i>Proceedings of the ECMLPKDD 2020</i>, 2020. <a href=\"https://doi.org/10.1007/978-3-030-66770-2_8\">https://doi.org/10.1007/978-3-030-66770-2_8</a>.","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: <a href=\"https://doi.org/10.1007/978-3-030-66770-2_8\">10.1007/978-3-030-66770-2_8</a>."}},{"language":[{"iso":"eng"}],"_id":"17605","project":[{"_id":"39","name":"InterGramm"}],"department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"user_id":"5786","abstract":[{"lang":"eng","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."}],"status":"public","publication":"Journal of Data Mining and Digital Humanities","type":"preprint","title":"Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction","main_file_link":[{"url":"https://arxiv.org/abs/2008.01377","open_access":"1"}],"date_updated":"2022-01-06T06:53:15Z","oa":"1","publisher":"episciences","author":[{"first_name":"Stefan Helmut","id":"39640","full_name":"Heid, Stefan Helmut","last_name":"Heid","orcid":"0000-0002-9461-7372"},{"first_name":"Marcel Dominik","full_name":"Wever, Marcel Dominik","id":"33176","orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever"},{"first_name":"Eyke","id":"48129","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier"}],"date_created":"2020-08-05T06:52:53Z","year":"2020","citation":{"ama":"Heid SH, Wever MD, Hüllermeier E. Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction. <i>Journal of Data Mining and Digital Humanities</i>.","ieee":"S. H. Heid, M. D. Wever, and E. Hüllermeier, “Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction,” <i>Journal of Data Mining and Digital Humanities</i>. episciences.","chicago":"Heid, Stefan Helmut, Marcel Dominik Wever, and Eyke Hüllermeier. “Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction.” <i>Journal of Data Mining and Digital Humanities</i>. episciences, n.d.","apa":"Heid, S. H., Wever, M. D., &#38; Hüllermeier, E. (n.d.). Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction. In <i>Journal of Data Mining and Digital Humanities</i>. episciences.","short":"S.H. Heid, M.D. Wever, E. Hüllermeier, Journal of Data Mining and Digital Humanities (n.d.).","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} }","mla":"Heid, Stefan Helmut, et al. “Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction.” <i>Journal of Data Mining and Digital Humanities</i>, episciences."},"publication_status":"submitted"}]