[{"status":"public","conference":{"end_date":"2021-10-13","start_date":"2021-10-11","name":"24th International Conference on Discovery Science","location":"Halifax, Canada"},"page":"166-180","_id":"27381","publisher":"Springer","user_id":"48192","volume":12986,"editor":[{"last_name":"Soares","first_name":"Carlos","full_name":"Soares, Carlos"},{"last_name":"Torgo","first_name":"Luis","full_name":"Torgo, Luis"}],"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>","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.","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>","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>."},"quality_controlled":"1","external_id":{"arxiv":["2104.08869"]},"title":"Ranking Structured Objects with Graph Neural Networks","year":"2021","author":[{"id":"48192","orcid":"0000-0002-0455-0048","first_name":"Clemens","last_name":"Damke","full_name":"Damke, Clemens"},{"id":"48129","full_name":"Hüllermeier, Eyke","first_name":"Eyke","last_name":"Hüllermeier"}],"publication_identifier":{"isbn":["9783030889418","9783030889425"],"issn":["0302-9743","1611-3349"]},"publication_status":"published","date_updated":"2022-04-11T22:08:12Z","intvolume":"     12986","language":[{"iso":"eng"}],"series_title":"Lecture Notes in Computer Science","doi":"10.1007/978-3-030-88942-5","publication":"Proceedings of The 24th International Conference on Discovery Science (DS 2021)","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"}],"date_created":"2021-11-11T14:15:18Z","keyword":["Graph-structured data","Graph neural networks","Preference learning","Learning to rank"],"type":"conference","department":[{"_id":"355"}]},{"department":[{"_id":"355"}],"type":"dissertation","date_created":"2021-11-08T14:05:19Z","file":[{"file_id":"30886","content_type":"application/pdf","file_name":"dissertation_publish_upload.pdf","access_level":"open_access","file_size":8098177,"relation":"main_file","date_updated":"2022-04-13T09:39:56Z","date_created":"2022-04-13T09:35:25Z","creator":"wever"}],"publication_status":"published","date_updated":"2022-04-13T09:39:56Z","author":[{"id":"33176","last_name":"Wever","first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik"}],"title":"Automated Machine Learning for Multi-Label Classification","year":"2021","doi":"10.17619/UNIPB/1-1302","language":[{"iso":"eng"}],"project":[{"_id":"1","name":"SFB 901"},{"_id":"3","name":"SFB 901 - Project Area B"},{"name":"SFB 901 - Subproject B2","_id":"10"}],"citation":{"chicago":"Wever, Marcel Dominik. <i>Automated Machine Learning for Multi-Label Classification</i>, 2021. <a href=\"https://doi.org/10.17619/UNIPB/1-1302\">https://doi.org/10.17619/UNIPB/1-1302</a>.","ama":"Wever MD. <i>Automated Machine Learning for Multi-Label Classification</i>.; 2021. doi:<a href=\"https://doi.org/10.17619/UNIPB/1-1302\">10.17619/UNIPB/1-1302</a>","short":"M.D. Wever, Automated Machine Learning for Multi-Label Classification, 2021.","bibtex":"@book{Wever_2021, title={Automated Machine Learning for Multi-Label Classification}, DOI={<a href=\"https://doi.org/10.17619/UNIPB/1-1302\">10.17619/UNIPB/1-1302</a>}, author={Wever, Marcel Dominik}, year={2021} }","mla":"Wever, Marcel Dominik. <i>Automated Machine Learning for Multi-Label Classification</i>. 2021, doi:<a href=\"https://doi.org/10.17619/UNIPB/1-1302\">10.17619/UNIPB/1-1302</a>.","apa":"Wever, M. D. (2021). <i>Automated Machine Learning for Multi-Label Classification</i>. <a href=\"https://doi.org/10.17619/UNIPB/1-1302\">https://doi.org/10.17619/UNIPB/1-1302</a>","ieee":"M. D. Wever, <i>Automated Machine Learning for Multi-Label Classification</i>. 2021."},"supervisor":[{"id":"48129","first_name":"Eyke","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke"}],"file_date_updated":"2022-04-13T09:39:56Z","oa":"1","has_accepted_license":"1","status":"public","user_id":"33176","ddc":["000"],"_id":"27284"},{"date_created":"2021-02-09T09:30:14Z","type":"conference","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"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.","mla":"Hanselle, Jonas Manuel, et al. <i>Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data</i>. 2021.","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} }","ama":"Hanselle JM, Tornede A, Wever MD, Hüllermeier E. Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data. Published online 2021.","short":"J.M. Hanselle, A. Tornede, M.D. Wever, E. Hüllermeier, (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."},"project":[{"name":"SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - Project Area B"},{"name":"SFB 901 - Subproject B2","_id":"10"},{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"language":[{"iso":"eng"}],"_id":"21198","series_title":"PAKDD","user_id":"38209","year":"2021","title":"Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data","status":"public","conference":{"end_date":"2021-05-14","start_date":"2021-05-11","name":"The 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2021)","location":"Delhi, India"},"author":[{"id":"43980","full_name":"Hanselle, Jonas Manuel","first_name":"Jonas Manuel","orcid":"0000-0002-1231-4985","last_name":"Hanselle"},{"last_name":"Tornede","first_name":"Alexander","full_name":"Tornede, Alexander","id":"38209"},{"full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","first_name":"Marcel Dominik","id":"33176"},{"id":"48129","last_name":"Hüllermeier","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}],"date_updated":"2022-08-24T12:49:06Z"},{"language":[{"iso":"eng"}],"_id":"19521","user_id":"13472","doi":"10.1007/978-3-030-58285-2_30","year":"2020","status":"public","title":"Learning Choice Functions via Pareto-Embeddings","author":[{"full_name":"Pfannschmidt, Karlson","last_name":"Pfannschmidt","first_name":"Karlson"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","last_name":"Hüllermeier"}],"publication_identifier":{"isbn":["9783030582845","9783030582852"],"issn":["0302-9743","1611-3349"]},"publication_status":"published","date_updated":"2022-01-06T06:54:06Z","date_created":"2020-09-17T10:52:41Z","place":"Cham","type":"book_chapter","department":[{"_id":"7"},{"_id":"355"}],"publication":"Lecture Notes in Computer Science","citation":{"bibtex":"@inbook{Pfannschmidt_Hüllermeier_2020, place={Cham}, title={Learning Choice Functions via Pareto-Embeddings}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-58285-2_30\">10.1007/978-3-030-58285-2_30</a>}, booktitle={Lecture Notes in Computer Science}, author={Pfannschmidt, Karlson and Hüllermeier, Eyke}, year={2020} }","short":"K. Pfannschmidt, E. Hüllermeier, in: Lecture Notes in Computer Science, Cham, 2020.","ama":"Pfannschmidt K, Hüllermeier E. Learning Choice Functions via Pareto-Embeddings. In: <i>Lecture Notes in Computer Science</i>. Cham; 2020. doi:<a href=\"https://doi.org/10.1007/978-3-030-58285-2_30\">10.1007/978-3-030-58285-2_30</a>","chicago":"Pfannschmidt, Karlson, and Eyke Hüllermeier. “Learning Choice Functions via Pareto-Embeddings.” In <i>Lecture Notes in Computer Science</i>. Cham, 2020. <a href=\"https://doi.org/10.1007/978-3-030-58285-2_30\">https://doi.org/10.1007/978-3-030-58285-2_30</a>.","ieee":"K. Pfannschmidt and E. Hüllermeier, “Learning Choice Functions via Pareto-Embeddings,” in <i>Lecture Notes in Computer Science</i>, Cham, 2020.","mla":"Pfannschmidt, Karlson, and Eyke Hüllermeier. “Learning Choice Functions via Pareto-Embeddings.” <i>Lecture Notes in Computer Science</i>, 2020, doi:<a href=\"https://doi.org/10.1007/978-3-030-58285-2_30\">10.1007/978-3-030-58285-2_30</a>.","apa":"Pfannschmidt, K., &#38; Hüllermeier, E. (2020). Learning Choice Functions via Pareto-Embeddings. In <i>Lecture Notes in Computer Science</i>. Cham. <a href=\"https://doi.org/10.1007/978-3-030-58285-2_30\">https://doi.org/10.1007/978-3-030-58285-2_30</a>"},"project":[{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}]},{"date_updated":"2022-01-06T06:54:17Z","publication_status":"published","intvolume":"       129","title":"A Novel Higher-order Weisfeiler-Lehman Graph Convolution","year":"2020","author":[{"id":"48192","full_name":"Damke, Clemens","orcid":"0000-0002-0455-0048","last_name":"Damke","first_name":"Clemens"},{"full_name":"Melnikov, Vitaly","first_name":"Vitaly","last_name":"Melnikov","id":"58747"},{"id":"48129","last_name":"Hüllermeier","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}],"series_title":"Proceedings of Machine Learning Research","language":[{"iso":"eng"}],"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."}],"publication":"Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020)","keyword":["graph neural networks","Weisfeiler-Lehman test","cycle detection"],"type":"conference","department":[{"_id":"355"}],"file":[{"creator":"cdamke","date_created":"2020-10-08T10:54:48Z","date_updated":"2020-10-08T11:21:00Z","relation":"main_file","access_level":"open_access","file_size":771137,"file_name":"damke20.pdf","content_type":"application/pdf","file_id":"19954"},{"file_id":"19955","content_type":"application/pdf","file_name":"damke20-supp.pdf","file_size":613163,"access_level":"open_access","relation":"supplementary_material","date_updated":"2020-10-08T11:24:29Z","date_created":"2020-10-08T10:54:59Z","creator":"cdamke"}],"date_created":"2020-10-08T10:48:38Z","has_accepted_license":"1","status":"public","conference":{"end_date":"2020-11-20","location":"Bangkok, Thailand","name":"Asian Conference on Machine Learning","start_date":"2020-11-18"},"ddc":["006"],"user_id":"48192","editor":[{"first_name":"Sinno","last_name":"Jialin Pan","full_name":"Jialin Pan, Sinno"},{"full_name":"Sugiyama, Masashi","last_name":"Sugiyama","first_name":"Masashi"}],"volume":129,"page":"49-64","_id":"19953","publisher":"PMLR","quality_controlled":"1","file_date_updated":"2020-10-08T11:24:29Z","citation":{"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.","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.","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.","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} }","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."},"oa":"1","external_id":{"arxiv":["2007.00346"]},"place":"Bangkok, Thailand"},{"user_id":"76599","page":"778-787","_id":"21534","language":[{"iso":"eng"}],"date_updated":"2022-01-06T06:55:03Z","status":"public","title":"Preselection Bandits","year":"2020","author":[{"full_name":"Bengs, Viktor","last_name":"Bengs","first_name":"Viktor"},{"full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","first_name":"Eyke"}],"type":"conference","department":[{"_id":"34"},{"_id":"7"},{"_id":"355"}],"date_created":"2021-03-18T11:13:12Z","project":[{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"publication":"International Conference on Machine Learning","citation":{"mla":"Bengs, Viktor, and Eyke Hüllermeier. “Preselection Bandits.” <i>International Conference on Machine Learning</i>, 2020, pp. 778–87.","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} }","ama":"Bengs V, Hüllermeier E. Preselection Bandits. In: <i>International Conference on Machine Learning</i>. ; 2020:778-787.","ieee":"V. Bengs and E. Hüllermeier, “Preselection Bandits,” in <i>International Conference on Machine Learning</i>, 2020, pp. 778–787.","apa":"Bengs, V., &#38; Hüllermeier, E. (2020). Preselection Bandits. In <i>International Conference on Machine Learning</i> (pp. 778–787).","chicago":"Bengs, Viktor, and Eyke Hüllermeier. “Preselection Bandits.” In <i>International Conference on Machine Learning</i>, 778–87, 2020.","short":"V. Bengs, E. Hüllermeier, in: International Conference on Machine Learning, 2020, pp. 778–787."}},{"date_updated":"2022-01-06T06:55:03Z","author":[{"full_name":"Bengs, Viktor","last_name":"Bengs","first_name":"Viktor"},{"full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","first_name":"Eyke"}],"status":"public","title":"Multi-Armed Bandits with Censored Consumption of Resources","year":"2020","user_id":"76599","_id":"21536","language":[{"iso":"eng"}],"project":[{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"abstract":[{"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.","lang":"eng"}],"citation":{"ieee":"V. Bengs and E. Hüllermeier, “Multi-Armed Bandits with Censored Consumption of Resources,” <i>arXiv:2011.00813</i>. 2020.","apa":"Bengs, V., &#38; Hüllermeier, E. (2020). Multi-Armed Bandits with Censored Consumption of Resources. <i>ArXiv:2011.00813</i>.","short":"V. Bengs, E. Hüllermeier, ArXiv:2011.00813 (2020).","chicago":"Bengs, Viktor, and Eyke Hüllermeier. “Multi-Armed Bandits with Censored Consumption of Resources.” <i>ArXiv:2011.00813</i>, 2020.","mla":"Bengs, Viktor, and Eyke Hüllermeier. “Multi-Armed Bandits with Censored Consumption of Resources.” <i>ArXiv:2011.00813</i>, 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} }","ama":"Bengs V, Hüllermeier E. Multi-Armed Bandits with Censored Consumption of Resources. <i>arXiv:201100813</i>. 2020."},"publication":"arXiv:2011.00813","department":[{"_id":"34"},{"_id":"7"},{"_id":"355"}],"type":"preprint","date_created":"2021-03-18T11:27:37Z"},{"date_created":"2020-07-21T10:06:51Z","type":"conference","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"publication":"Discovery Science","citation":{"chicago":"Tornede, Alexander, Marcel Dominik Wever, and Eyke Hüllermeier. “Extreme Algorithm Selection with Dyadic Feature Representation.” In <i>Discovery Science</i>, 2020.","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.","apa":"Tornede, A., Wever, M. D., &#38; Hüllermeier, E. (2020). Extreme Algorithm Selection with Dyadic Feature Representation. <i>Discovery Science</i>. 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} }","ama":"Tornede A, Wever MD, Hüllermeier E. Extreme Algorithm Selection with Dyadic Feature Representation. In: <i>Discovery Science</i>. ; 2020.","mla":"Tornede, Alexander, et al. “Extreme Algorithm Selection with Dyadic Feature Representation.” <i>Discovery Science</i>, 2020."},"project":[{"name":"SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - Project Area B"},{"name":"SFB 901 - Subproject B2","_id":"10"},{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"language":[{"iso":"eng"}],"_id":"17407","user_id":"5786","status":"public","title":"Extreme Algorithm Selection with Dyadic Feature Representation","year":"2020","author":[{"full_name":"Tornede, Alexander","first_name":"Alexander","last_name":"Tornede","id":"38209"},{"full_name":"Wever, Marcel Dominik","first_name":"Marcel Dominik","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818","id":"33176"},{"id":"48129","last_name":"Hüllermeier","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}],"conference":{"name":"Discovery Science 2020"},"date_updated":"2022-01-06T06:53:10Z"},{"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"}],"citation":{"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} }","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.","mla":"Hanselle, Jonas Manuel, et al. “Hybrid Ranking and Regression for Algorithm Selection.” <i>KI 2020: Advances in Artificial Intelligence</i>, 2020.","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.","short":"J.M. Hanselle, A. Tornede, M.D. Wever, E. Hüllermeier, in: KI 2020: Advances in Artificial Intelligence, 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.","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."},"publication":"KI 2020: Advances in Artificial Intelligence","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"type":"conference","date_created":"2020-07-21T10:21:09Z","date_updated":"2022-01-06T06:53:10Z","author":[{"id":"43980","first_name":"Jonas Manuel","orcid":"0000-0002-1231-4985","last_name":"Hanselle","full_name":"Hanselle, Jonas Manuel"},{"id":"38209","full_name":"Tornede, Alexander","first_name":"Alexander","last_name":"Tornede"},{"full_name":"Wever, Marcel Dominik","last_name":"Wever","first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","id":"33176"},{"id":"48129","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","first_name":"Eyke"}],"conference":{"name":"43rd German Conference on Artificial Intelligence"},"status":"public","title":"Hybrid Ranking and Regression for Algorithm Selection","year":"2020","user_id":"5786","language":[{"iso":"eng"}],"_id":"17408"},{"doi":"10.1007/978-3-030-66770-2_8","user_id":"5786","language":[{"iso":"eng"}],"_id":"17424","date_updated":"2022-01-06T06:53:11Z","conference":{"name":"IOTStream Workshop @ ECMLPKDD 2020"},"author":[{"id":"40795","full_name":"Tornede, Tanja","last_name":"Tornede","first_name":"Tanja"},{"full_name":"Tornede, Alexander","first_name":"Alexander","last_name":"Tornede","id":"38209"},{"last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik","full_name":"Wever, Marcel Dominik","id":"33176"},{"last_name":"Mohr","first_name":"Felix","full_name":"Mohr, Felix"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","last_name":"Hüllermeier","id":"48129"}],"title":"AutoML for Predictive Maintenance: One Tool to RUL Them All","year":"2020","status":"public","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"type":"conference","date_created":"2020-07-28T09:17:41Z","project":[{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"},{"name":"SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - Subproject B2","_id":"10"},{"name":"SFB 901","_id":"1"},{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"citation":{"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} }","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>","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>.","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>.","short":"T. Tornede, A. Tornede, M.D. Wever, F. Mohr, E. Hüllermeier, in: Proceedings of the ECMLPKDD 2020, 2020.","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>.","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>"},"publication":"Proceedings of the ECMLPKDD 2020"},{"user_id":"5786","_id":"17605","language":[{"iso":"eng"}],"publisher":"episciences","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2008.01377"}],"date_updated":"2022-01-06T06:53:15Z","publication_status":"submitted","author":[{"id":"39640","last_name":"Heid","first_name":"Stefan Helmut","orcid":"0000-0002-9461-7372","full_name":"Heid, Stefan Helmut"},{"id":"33176","orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik","last_name":"Wever","full_name":"Wever, Marcel Dominik"},{"id":"48129","first_name":"Eyke","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke"}],"year":"2020","status":"public","title":"Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction","oa":"1","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"type":"preprint","date_created":"2020-08-05T06:52:53Z","project":[{"name":"InterGramm","_id":"39"}],"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."}],"citation":{"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.","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} }","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.","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.).","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."},"publication":"Journal of Data Mining and Digital Humanities"},{"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"}],"citation":{"ieee":"A. Tornede, M. D. Wever, and E. Hüllermeier, “Towards Meta-Algorithm Selection,” presented at the Workshop MetaLearn 2020 @ NeurIPS 2020, Online, 2020.","apa":"Tornede, A., Wever, M. D., &#38; Hüllermeier, E. (2020). Towards Meta-Algorithm Selection. <i>Workshop MetaLearn 2020 @ NeurIPS 2020</i>. Workshop MetaLearn 2020 @ NeurIPS 2020, Online.","chicago":"Tornede, Alexander, Marcel Dominik Wever, and Eyke Hüllermeier. “Towards Meta-Algorithm Selection.” In <i>Workshop MetaLearn 2020 @ NeurIPS 2020</i>, 2020.","short":"A. Tornede, M.D. Wever, E. Hüllermeier, in: Workshop MetaLearn 2020 @ NeurIPS 2020, 2020.","mla":"Tornede, Alexander, et al. “Towards Meta-Algorithm Selection.” <i>Workshop MetaLearn 2020 @ NeurIPS 2020</i>, 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} }","ama":"Tornede A, Wever MD, Hüllermeier E. Towards Meta-Algorithm Selection. In: <i>Workshop MetaLearn 2020 @ NeurIPS 2020</i>. ; 2020."},"publication":"Workshop MetaLearn 2020 @ NeurIPS 2020","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"type":"conference","date_created":"2020-11-06T09:42:27Z","date_updated":"2022-01-06T06:54:26Z","author":[{"first_name":"Alexander","last_name":"Tornede","full_name":"Tornede, Alexander","id":"38209"},{"full_name":"Wever, Marcel Dominik","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik","id":"33176"},{"id":"48129","last_name":"Hüllermeier","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}],"conference":{"location":"Online","name":"Workshop MetaLearn 2020 @ NeurIPS 2020"},"title":"Towards Meta-Algorithm Selection","status":"public","year":"2020","user_id":"5786","language":[{"iso":"eng"}],"_id":"20306"},{"department":[{"_id":"34"},{"_id":"7"},{"_id":"355"}],"type":"book_chapter","date_created":"2020-08-17T11:44:37Z","publication":"Learning and Intelligent Optimization. LION 2020.","doi":"10.1007/978-3-030-53552-0_22","language":[{"iso":"eng"}],"series_title":"Lecture Notes in Computer Science","intvolume":"     12096","date_updated":"2022-01-06T06:53:25Z","publication_status":"published","publication_identifier":{"issn":["0302-9743","1611-3349"],"isbn":["9783030535513","9783030535520"]},"author":[{"first_name":"Adil","last_name":"El Mesaoudi-Paul","full_name":"El Mesaoudi-Paul, Adil"},{"last_name":"Weiß","first_name":"Dimitri","full_name":"Weiß, Dimitri"},{"full_name":"Bengs, Viktor","first_name":"Viktor","last_name":"Bengs","id":"76599"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","last_name":"Hüllermeier","id":"48129"},{"full_name":"Tierney, Kevin","first_name":"Kevin","last_name":"Tierney"}],"title":"Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach","year":"2020","place":"Cham","project":[{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"citation":{"ama":"El Mesaoudi-Paul A, Weiß D, Bengs V, Hüllermeier E, Tierney K. Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach. In: <i>Learning and Intelligent Optimization. LION 2020.</i> Vol 12096. Lecture Notes in Computer Science. Cham: Springer; 2020:216-232. doi:<a href=\"https://doi.org/10.1007/978-3-030-53552-0_22\">10.1007/978-3-030-53552-0_22</a>","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={<a href=\"https://doi.org/10.1007/978-3-030-53552-0_22\">10.1007/978-3-030-53552-0_22</a>}, 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} }","mla":"El Mesaoudi-Paul, Adil, et al. “Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach.” <i>Learning and Intelligent Optimization. LION 2020.</i>, vol. 12096, Springer, 2020, pp. 216–32, doi:<a href=\"https://doi.org/10.1007/978-3-030-53552-0_22\">10.1007/978-3-030-53552-0_22</a>.","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.","chicago":"El Mesaoudi-Paul, Adil, Dimitri Weiß, Viktor Bengs, Eyke Hüllermeier, and Kevin Tierney. “Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach.” In <i>Learning and Intelligent Optimization. LION 2020.</i>, 12096:216–32. Lecture Notes in Computer Science. Cham: Springer, 2020. <a href=\"https://doi.org/10.1007/978-3-030-53552-0_22\">https://doi.org/10.1007/978-3-030-53552-0_22</a>.","apa":"El Mesaoudi-Paul, A., Weiß, D., Bengs, V., Hüllermeier, E., &#38; Tierney, K. (2020). Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach. In <i>Learning and Intelligent Optimization. LION 2020.</i> (Vol. 12096, pp. 216–232). Cham: Springer. <a href=\"https://doi.org/10.1007/978-3-030-53552-0_22\">https://doi.org/10.1007/978-3-030-53552-0_22</a>","ieee":"A. El Mesaoudi-Paul, D. Weiß, V. Bengs, E. Hüllermeier, and K. Tierney, “Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach,” in <i>Learning and Intelligent Optimization. LION 2020.</i>, vol. 12096, Cham: Springer, 2020, pp. 216–232."},"volume":12096,"user_id":"76599","publisher":"Springer","_id":"18014","page":"216 - 232","status":"public"},{"project":[{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"abstract":[{"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.","lang":"eng"}],"citation":{"ieee":"A. El Mesaoudi-Paul, V. Bengs, and E. Hüllermeier, “Online Preselection with Context Information under the Plackett-Luce  Model,” <i>arXiv:2002.04275</i>. .","apa":"El Mesaoudi-Paul, A., Bengs, V., &#38; Hüllermeier, E. (n.d.). Online Preselection with Context Information under the Plackett-Luce  Model. <i>ArXiv:2002.04275</i>.","mla":"El Mesaoudi-Paul, Adil, et al. “Online Preselection with Context Information under the Plackett-Luce  Model.” <i>ArXiv:2002.04275</i>.","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} }","chicago":"El Mesaoudi-Paul, Adil, Viktor Bengs, and Eyke Hüllermeier. “Online Preselection with Context Information under the Plackett-Luce  Model.” <i>ArXiv:2002.04275</i>, n.d.","ama":"El Mesaoudi-Paul A, Bengs V, Hüllermeier E. Online Preselection with Context Information under the Plackett-Luce  Model. <i>arXiv:200204275</i>.","short":"A. El Mesaoudi-Paul, V. Bengs, E. Hüllermeier, ArXiv:2002.04275 (n.d.)."},"publication":"arXiv:2002.04275","department":[{"_id":"34"},{"_id":"7"},{"_id":"355"}],"type":"preprint","date_created":"2020-08-17T11:49:40Z","date_updated":"2022-01-06T06:53:25Z","publication_status":"draft","author":[{"first_name":"Adil","last_name":"El Mesaoudi-Paul","full_name":"El Mesaoudi-Paul, Adil"},{"first_name":"Viktor","last_name":"Bengs","full_name":"Bengs, Viktor","id":"76599"},{"id":"48129","first_name":"Eyke","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke"}],"title":"Online Preselection with Context Information under the Plackett-Luce  Model","status":"public","year":"2020","user_id":"76599","_id":"18017","language":[{"iso":"eng"}]},{"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"}],"project":[{"name":"SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - Project Area B"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"publication":"ACML 2020","citation":{"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} }","ama":"Tornede A, Wever MD, Werner S, Mohr F, Hüllermeier E. Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. In: <i>ACML 2020</i>. ; 2020.","mla":"Tornede, Alexander, et al. “Run2Survive: A Decision-Theoretic Approach to Algorithm Selection Based on Survival Analysis.” <i>ACML 2020</i>, 2020.","short":"A. Tornede, M.D. Wever, S. Werner, F. Mohr, E. Hüllermeier, in: ACML 2020, 2020.","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 <i>ACML 2020</i>, 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.","apa":"Tornede, A., Wever, M. D., Werner, S., Mohr, F., &#38; Hüllermeier, E. (2020). Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. <i>ACML 2020</i>. 12th Asian Conference on Machine Learning, Bangkok, Thailand."},"type":"conference","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"date_created":"2020-08-25T12:09:28Z","date_updated":"2022-01-06T06:53:28Z","year":"2020","status":"public","title":"Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis","author":[{"last_name":"Tornede","first_name":"Alexander","full_name":"Tornede, Alexander","id":"38209"},{"id":"33176","orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","first_name":"Marcel Dominik","full_name":"Wever, Marcel Dominik"},{"first_name":"Stefan","last_name":"Werner","full_name":"Werner, Stefan"},{"full_name":"Mohr, Felix","last_name":"Mohr","first_name":"Felix"},{"id":"48129","first_name":"Eyke","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke"}],"conference":{"location":"Bangkok, Thailand","name":"12th Asian Conference on Machine Learning","start_date":"2020-11-18","end_date":"2020-11-20"},"user_id":"5786","main_file_link":[{"url":"https://arxiv.org/pdf/2007.02816.pdf"}],"_id":"18276","language":[{"iso":"eng"}]},{"_id":"16725","publisher":"Springer","language":[{"iso":"eng"}],"user_id":"477","year":"2020","title":"Algorithm Selection for Software Validation Based on Graph Kernels","status":"public","author":[{"id":"50003","full_name":"Richter, Cedric","last_name":"Richter","first_name":"Cedric"},{"first_name":"Eyke","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","id":"48129"},{"full_name":"Jakobs, Marie-Christine","first_name":"Marie-Christine","last_name":"Jakobs"},{"id":"573","full_name":"Wehrheim, Heike","first_name":"Heike","last_name":"Wehrheim"}],"publication_status":"accepted","date_updated":"2022-01-06T06:52:55Z","date_created":"2020-04-19T14:08:06Z","type":"journal_article","department":[{"_id":"7"},{"_id":"77"},{"_id":"355"}],"publication":"Journal of Automated Software Engineering","citation":{"mla":"Richter, Cedric, et al. “Algorithm Selection for Software Validation Based on Graph Kernels.” <i>Journal of Automated Software Engineering</i>, 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} }","ama":"Richter C, Hüllermeier E, Jakobs M-C, Wehrheim H. Algorithm Selection for Software Validation Based on Graph Kernels. <i>Journal of Automated Software Engineering</i>.","ieee":"C. Richter, E. Hüllermeier, M.-C. Jakobs, and H. Wehrheim, “Algorithm Selection for Software Validation Based on Graph Kernels,” <i>Journal of Automated Software Engineering</i>.","apa":"Richter, C., Hüllermeier, E., Jakobs, M.-C., &#38; Wehrheim, H. (n.d.). Algorithm Selection for Software Validation Based on Graph Kernels. <i>Journal of Automated Software Engineering</i>.","chicago":"Richter, Cedric, Eyke Hüllermeier, Marie-Christine Jakobs, and Heike Wehrheim. “Algorithm Selection for Software Validation Based on Graph Kernels.” <i>Journal of Automated Software Engineering</i>, n.d.","short":"C. Richter, E. Hüllermeier, M.-C. Jakobs, H. Wehrheim, Journal of Automated Software Engineering (n.d.)."},"project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Project Area B","_id":"3"},{"_id":"11","name":"SFB 901 - Subproject B3"},{"_id":"12","name":"SFB 901 - Subproject B4"}]},{"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"}],"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."}],"citation":{"mla":"Wever, Marcel Dominik, et al. <i>LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification</i>. Springer.","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.","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} }","apa":"Wever, M. D., Tornede, A., Mohr, F., &#38; Hüllermeier, E. (n.d.). <i>LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification</i>. Symposium on Intelligent Data Analysis, Konstanz, Germany.","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.","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.","short":"M.D. Wever, A. Tornede, F. Mohr, E. Hüllermeier, in: Springer, n.d."},"department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"type":"conference","date_created":"2020-01-23T08:44:08Z","publication_status":"accepted","date_updated":"2022-01-06T06:52:30Z","author":[{"orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik","last_name":"Wever","full_name":"Wever, Marcel Dominik","id":"33176"},{"full_name":"Tornede, Alexander","first_name":"Alexander","last_name":"Tornede","id":"38209"},{"full_name":"Mohr, Felix","last_name":"Mohr","first_name":"Felix"},{"id":"48129","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","first_name":"Eyke"}],"conference":{"end_date":"2020-04-27","name":"Symposium on Intelligent Data Analysis","start_date":"2020-04-24","location":"Konstanz, Germany"},"year":"2020","title":"LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification","status":"public","user_id":"5786","publisher":"Springer","_id":"15629","language":[{"iso":"eng"}]},{"doi":"10.1162/evco_a_00266","language":[{"iso":"eng"}],"date_updated":"2022-01-06T06:52:15Z","publication_status":"published","intvolume":"        28","title":"Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets","year":"2020","author":[{"full_name":"Wever, Marcel Dominik","first_name":"Marcel Dominik","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818","id":"33176"},{"last_name":"van Rooijen","first_name":"Lorijn","full_name":"van Rooijen, Lorijn","id":"58843"},{"first_name":"Heiko","last_name":"Hamann","full_name":"Hamann, Heiko"}],"type":"journal_article","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"},{"_id":"63"},{"_id":"238"}],"date_created":"2019-11-18T14:19:19Z","related_material":{"link":[{"url":"https://www.mitpressjournals.org/doi/pdf/10.1162/evco_a_00266","relation":"confirmation"}]},"abstract":[{"lang":"eng","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."}],"issue":"2","publication":"Evolutionary Computation","user_id":"15415","volume":28,"page":"165–193","publisher":"MIT Press Journals","_id":"15025","status":"public","project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Project Area B","_id":"3"},{"_id":"9","name":"SFB 901 - Subproject B1"},{"name":"SFB 901 - Subproject B2","_id":"10"},{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"citation":{"short":"M.D. Wever, L. van Rooijen, H. Hamann, Evolutionary Computation 28 (2020) 165–193.","chicago":"Wever, Marcel Dominik, Lorijn van Rooijen, and Heiko Hamann. “Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets.” <i>Evolutionary Computation</i> 28, no. 2 (2020): 165–193. <a href=\"https://doi.org/10.1162/evco_a_00266\">https://doi.org/10.1162/evco_a_00266</a>.","ieee":"M. D. Wever, L. van Rooijen, and H. Hamann, “Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets,” <i>Evolutionary Computation</i>, vol. 28, no. 2, pp. 165–193, 2020, doi: <a href=\"https://doi.org/10.1162/evco_a_00266\">10.1162/evco_a_00266</a>.","apa":"Wever, M. D., van Rooijen, L., &#38; Hamann, H. (2020). Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets. <i>Evolutionary Computation</i>, <i>28</i>(2), 165–193. <a href=\"https://doi.org/10.1162/evco_a_00266\">https://doi.org/10.1162/evco_a_00266</a>","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={<a href=\"https://doi.org/10.1162/evco_a_00266\">10.1162/evco_a_00266</a>}, 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} }","ama":"Wever MD, van Rooijen L, Hamann H. Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets. <i>Evolutionary Computation</i>. 2020;28(2):165–193. doi:<a href=\"https://doi.org/10.1162/evco_a_00266\">10.1162/evco_a_00266</a>","mla":"Wever, Marcel Dominik, et al. “Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets.” <i>Evolutionary Computation</i>, vol. 28, no. 2, MIT Press Journals, 2020, pp. 165–193, doi:<a href=\"https://doi.org/10.1162/evco_a_00266\">10.1162/evco_a_00266</a>."}},{"date_created":"2020-09-17T10:53:38Z","department":[{"_id":"7"},{"_id":"355"}],"type":"preprint","citation":{"mla":"Pfannschmidt, Karlson, et al. “Learning Choice Functions: Concepts and Architectures.” <i>ArXiv:1901.10860</i>, 2019.","ama":"Pfannschmidt K, Gupta P, Hüllermeier E. Learning Choice Functions: Concepts and Architectures. <i>arXiv:190110860</i>. 2019.","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} }","apa":"Pfannschmidt, K., Gupta, P., &#38; Hüllermeier, E. (2019). Learning Choice Functions: Concepts and Architectures. <i>ArXiv:1901.10860</i>.","ieee":"K. Pfannschmidt, P. Gupta, and E. Hüllermeier, “Learning Choice Functions: Concepts and Architectures,” <i>arXiv:1901.10860</i>. 2019.","chicago":"Pfannschmidt, Karlson, Pritha Gupta, and Eyke Hüllermeier. “Learning Choice Functions: Concepts and Architectures.” <i>ArXiv:1901.10860</i>, 2019.","short":"K. Pfannschmidt, P. Gupta, E. Hüllermeier, ArXiv:1901.10860 (2019)."},"publication":"arXiv:1901.10860","project":[{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"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"}],"_id":"19523","language":[{"iso":"eng"}],"user_id":"13472","author":[{"first_name":"Karlson","last_name":"Pfannschmidt","full_name":"Pfannschmidt, Karlson"},{"full_name":"Gupta, Pritha","last_name":"Gupta","first_name":"Pritha"},{"last_name":"Hüllermeier","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}],"status":"public","year":"2019","title":"Learning Choice Functions: Concepts and Architectures","date_updated":"2022-01-06T06:54:06Z"},{"author":[{"first_name":"Marie-Luis","last_name":"Merten","full_name":"Merten, Marie-Luis"},{"full_name":"Seemann, Nina","first_name":"Nina","last_name":"Seemann"},{"id":"33176","full_name":"Wever, Marcel Dominik","first_name":"Marcel Dominik","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818"}],"title":"Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff","status":"public","year":"2019","date_updated":"2022-01-06T06:53:15Z","publication_status":"published","language":[{"iso":"ger"}],"_id":"17565","page":"124-146","user_id":"5786","citation":{"ieee":"M.-L. Merten, N. Seemann, and M. D. Wever, “Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff,” <i>Niederdeutsches Jahrbuch</i>, no. 142, pp. 124–146, 2019.","apa":"Merten, M.-L., Seemann, N., &#38; Wever, M. D. (2019). Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff. <i>Niederdeutsches Jahrbuch</i>, <i>142</i>, 124–146.","short":"M.-L. Merten, N. Seemann, M.D. Wever, Niederdeutsches Jahrbuch (2019) 124–146.","chicago":"Merten, Marie-Luis, Nina Seemann, and Marcel Dominik Wever. “Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff.” <i>Niederdeutsches Jahrbuch</i>, no. 142 (2019): 124–46.","mla":"Merten, Marie-Luis, et al. “Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff.” <i>Niederdeutsches Jahrbuch</i>, 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} }","ama":"Merten M-L, Seemann N, Wever MD. Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff. <i>Niederdeutsches Jahrbuch</i>. 2019;(142):124-146."},"issue":"142","publication":"Niederdeutsches Jahrbuch","project":[{"name":"InterGramm","_id":"39"}],"date_created":"2020-08-03T13:55:04Z","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"type":"journal_article"}]