--- _id: '27381' abstract: - lang: eng text: Graph neural networks (GNNs) have been successfully applied in many structured data domains, with applications ranging from molecular property prediction to the analysis of social networks. Motivated by the broad applicability of GNNs, we propose the family of so-called RankGNNs, a combination of neural Learning to Rank (LtR) methods and GNNs. RankGNNs are trained with a set of pair-wise preferences between graphs, suggesting that one of them is preferred over the other. One practical application of this problem is drug screening, where an expert wants to find the most promising molecules in a large collection of drug candidates. We empirically demonstrate that our proposed pair-wise RankGNN approach either significantly outperforms or at least matches the ranking performance of the naive point-wise baseline approach, in which the LtR problem is solved via GNN-based graph regression. author: - first_name: Clemens full_name: Damke, Clemens id: '48192' last_name: Damke orcid: 0000-0002-0455-0048 - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Damke C, Hüllermeier E. Ranking Structured Objects with Graph Neural Networks. In: Soares C, Torgo L, eds. Proceedings of The 24th International Conference on Discovery Science (DS 2021). Vol 12986. Lecture Notes in Computer Science. Springer; 2021:166-180. doi:10.1007/978-3-030-88942-5' apa: Damke, C., & Hüllermeier, E. (2021). Ranking Structured Objects with Graph Neural Networks. In C. Soares & L. Torgo (Eds.), Proceedings of The 24th International Conference on Discovery Science (DS 2021) (Vol. 12986, pp. 166–180). Springer. https://doi.org/10.1007/978-3-030-88942-5 bibtex: '@inproceedings{Damke_Hüllermeier_2021, series={Lecture Notes in Computer Science}, title={Ranking Structured Objects with Graph Neural Networks}, volume={12986}, DOI={10.1007/978-3-030-88942-5}, 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} }' chicago: Damke, Clemens, and Eyke Hüllermeier. “Ranking Structured Objects with Graph Neural Networks.” In Proceedings of The 24th International Conference on Discovery Science (DS 2021), edited by Carlos Soares and Luis Torgo, 12986:166–80. Lecture Notes in Computer Science. Springer, 2021. https://doi.org/10.1007/978-3-030-88942-5. ieee: 'C. Damke and E. Hüllermeier, “Ranking Structured Objects with Graph Neural Networks,” in Proceedings of The 24th International Conference on Discovery Science (DS 2021), Halifax, Canada, 2021, vol. 12986, pp. 166–180, doi: 10.1007/978-3-030-88942-5.' mla: Damke, Clemens, and Eyke Hüllermeier. “Ranking Structured Objects with Graph Neural Networks.” Proceedings of The 24th International Conference on Discovery Science (DS 2021), edited by Carlos Soares and Luis Torgo, vol. 12986, Springer, 2021, pp. 166–80, doi:10.1007/978-3-030-88942-5. 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.' conference: end_date: 2021-10-13 location: Halifax, Canada name: 24th International Conference on Discovery Science start_date: 2021-10-11 date_created: 2021-11-11T14:15:18Z date_updated: 2022-04-11T22:08:12Z department: - _id: '355' doi: 10.1007/978-3-030-88942-5 editor: - first_name: Carlos full_name: Soares, Carlos last_name: Soares - first_name: Luis full_name: Torgo, Luis last_name: Torgo external_id: arxiv: - '2104.08869' intvolume: ' 12986' keyword: - Graph-structured data - Graph neural networks - Preference learning - Learning to rank language: - iso: eng page: 166-180 publication: Proceedings of The 24th International Conference on Discovery Science (DS 2021) publication_identifier: isbn: - '9783030889418' - '9783030889425' issn: - 0302-9743 - 1611-3349 publication_status: published publisher: Springer quality_controlled: '1' series_title: Lecture Notes in Computer Science status: public title: Ranking Structured Objects with Graph Neural Networks type: conference user_id: '48192' volume: 12986 year: '2021' ... --- _id: '27284' author: - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' citation: ama: Wever MD. Automated Machine Learning for Multi-Label Classification.; 2021. doi:10.17619/UNIPB/1-1302 apa: Wever, M. D. (2021). Automated Machine Learning for Multi-Label Classification. https://doi.org/10.17619/UNIPB/1-1302 bibtex: '@book{Wever_2021, title={Automated Machine Learning for Multi-Label Classification}, DOI={10.17619/UNIPB/1-1302}, author={Wever, Marcel Dominik}, year={2021} }' chicago: Wever, Marcel Dominik. Automated Machine Learning for Multi-Label Classification, 2021. https://doi.org/10.17619/UNIPB/1-1302. ieee: M. D. Wever, Automated Machine Learning for Multi-Label Classification. 2021. mla: Wever, Marcel Dominik. Automated Machine Learning for Multi-Label Classification. 2021, doi:10.17619/UNIPB/1-1302. short: M.D. Wever, Automated Machine Learning for Multi-Label Classification, 2021. date_created: 2021-11-08T14:05:19Z date_updated: 2022-04-13T09:39:56Z ddc: - '000' department: - _id: '355' doi: 10.17619/UNIPB/1-1302 file: - access_level: open_access content_type: application/pdf creator: wever date_created: 2022-04-13T09:35:25Z date_updated: 2022-04-13T09:39:56Z file_id: '30886' file_name: dissertation_publish_upload.pdf file_size: 8098177 relation: main_file file_date_updated: 2022-04-13T09:39:56Z has_accepted_license: '1' language: - iso: eng oa: '1' project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 publication_status: published status: public supervisor: - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier title: Automated Machine Learning for Multi-Label Classification type: dissertation user_id: '33176' year: '2021' ... --- _id: '21198' author: - first_name: Jonas Manuel full_name: Hanselle, Jonas Manuel id: '43980' last_name: Hanselle orcid: 0000-0002-1231-4985 - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - 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 citation: ama: 'Hanselle JM, Tornede A, Wever MD, Hüllermeier E. Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data. Published online 2021.' apa: 'Hanselle, J. M., Tornede, A., Wever, M. D., & Hüllermeier, E. (2021). Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data. The 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2021), Delhi, India.' 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} }' 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.' mla: 'Hanselle, Jonas Manuel, et al. Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data. 2021.' short: J.M. Hanselle, A. Tornede, M.D. Wever, E. Hüllermeier, (2021). conference: end_date: 2021-05-14 location: Delhi, India name: The 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2021) start_date: 2021-05-11 date_created: 2021-02-09T09:30:14Z date_updated: 2022-08-24T12:49:06Z department: - _id: '34' - _id: '355' - _id: '26' language: - iso: eng 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 series_title: PAKDD status: public title: 'Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data' type: conference user_id: '38209' year: '2021' ... --- _id: '19521' author: - first_name: Karlson full_name: Pfannschmidt, Karlson last_name: Pfannschmidt - first_name: Eyke full_name: Hüllermeier, Eyke last_name: Hüllermeier citation: ama: 'Pfannschmidt K, Hüllermeier E. Learning Choice Functions via Pareto-Embeddings. In: Lecture Notes in Computer Science. Cham; 2020. doi:10.1007/978-3-030-58285-2_30' apa: Pfannschmidt, K., & Hüllermeier, E. (2020). Learning Choice Functions via Pareto-Embeddings. In Lecture Notes in Computer Science. Cham. https://doi.org/10.1007/978-3-030-58285-2_30 bibtex: '@inbook{Pfannschmidt_Hüllermeier_2020, place={Cham}, title={Learning Choice Functions via Pareto-Embeddings}, DOI={10.1007/978-3-030-58285-2_30}, booktitle={Lecture Notes in Computer Science}, author={Pfannschmidt, Karlson and Hüllermeier, Eyke}, year={2020} }' chicago: Pfannschmidt, Karlson, and Eyke Hüllermeier. “Learning Choice Functions via Pareto-Embeddings.” In Lecture Notes in Computer Science. Cham, 2020. https://doi.org/10.1007/978-3-030-58285-2_30. ieee: K. Pfannschmidt and E. Hüllermeier, “Learning Choice Functions via Pareto-Embeddings,” in Lecture Notes in Computer Science, Cham, 2020. mla: Pfannschmidt, Karlson, and Eyke Hüllermeier. “Learning Choice Functions via Pareto-Embeddings.” Lecture Notes in Computer Science, 2020, doi:10.1007/978-3-030-58285-2_30. short: 'K. Pfannschmidt, E. Hüllermeier, in: Lecture Notes in Computer Science, Cham, 2020.' date_created: 2020-09-17T10:52:41Z date_updated: 2022-01-06T06:54:06Z department: - _id: '7' - _id: '355' doi: 10.1007/978-3-030-58285-2_30 language: - iso: eng place: Cham project: - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: Lecture Notes in Computer Science publication_identifier: isbn: - '9783030582845' - '9783030582852' issn: - 0302-9743 - 1611-3349 publication_status: published status: public title: Learning Choice Functions via Pareto-Embeddings type: book_chapter user_id: '13472' year: '2020' ... --- _id: '19953' 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. author: - first_name: Clemens full_name: Damke, Clemens id: '48192' last_name: Damke orcid: 0000-0002-0455-0048 - first_name: Vitaly full_name: Melnikov, Vitaly id: '58747' last_name: Melnikov - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Damke C, Melnikov V, Hüllermeier E. A Novel Higher-order Weisfeiler-Lehman Graph Convolution. In: Jialin Pan S, Sugiyama M, eds. Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020). Vol 129. Proceedings of Machine Learning Research. Bangkok, Thailand: PMLR; 2020:49-64.' apa: 'Damke, C., Melnikov, V., & Hüllermeier, E. (2020). A Novel Higher-order Weisfeiler-Lehman Graph Convolution. In S. Jialin Pan & M. Sugiyama (Eds.), Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020) (Vol. 129, pp. 49–64). Bangkok, Thailand: PMLR.' 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} }' chicago: 'Damke, Clemens, Vitaly Melnikov, and Eyke Hüllermeier. “A Novel Higher-Order Weisfeiler-Lehman Graph Convolution.” In Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020), edited by Sinno Jialin Pan and Masashi Sugiyama, 129:49–64. Proceedings of Machine Learning Research. Bangkok, Thailand: PMLR, 2020.' ieee: C. Damke, V. Melnikov, and E. Hüllermeier, “A Novel Higher-order Weisfeiler-Lehman Graph Convolution,” in Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020), Bangkok, Thailand, 2020, vol. 129, pp. 49–64. mla: Damke, Clemens, et al. “A Novel Higher-Order Weisfeiler-Lehman Graph Convolution.” Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020), edited by Sinno Jialin Pan and Masashi Sugiyama, vol. 129, PMLR, 2020, pp. 49–64. 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.' conference: end_date: 2020-11-20 location: Bangkok, Thailand name: Asian Conference on Machine Learning start_date: 2020-11-18 date_created: 2020-10-08T10:48:38Z date_updated: 2022-01-06T06:54:17Z ddc: - '006' department: - _id: '355' editor: - first_name: Sinno full_name: Jialin Pan, Sinno last_name: Jialin Pan - first_name: Masashi full_name: Sugiyama, Masashi last_name: Sugiyama external_id: arxiv: - '2007.00346' file: - access_level: open_access content_type: application/pdf creator: cdamke date_created: 2020-10-08T10:54:48Z date_updated: 2020-10-08T11:21:00Z file_id: '19954' file_name: damke20.pdf file_size: 771137 relation: main_file - access_level: open_access content_type: application/pdf creator: cdamke date_created: 2020-10-08T10:54:59Z date_updated: 2020-10-08T11:24:29Z file_id: '19955' file_name: damke20-supp.pdf file_size: 613163 relation: supplementary_material file_date_updated: 2020-10-08T11:24:29Z has_accepted_license: '1' intvolume: ' 129' keyword: - graph neural networks - Weisfeiler-Lehman test - cycle detection language: - iso: eng oa: '1' page: 49-64 place: Bangkok, Thailand publication: Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020) publication_status: published publisher: PMLR quality_controlled: '1' series_title: Proceedings of Machine Learning Research status: public title: A Novel Higher-order Weisfeiler-Lehman Graph Convolution type: conference user_id: '48192' volume: 129 year: '2020' ... --- _id: '21534' author: - first_name: Viktor full_name: Bengs, Viktor last_name: Bengs - first_name: Eyke full_name: Hüllermeier, Eyke last_name: Hüllermeier citation: ama: 'Bengs V, Hüllermeier E. Preselection Bandits. In: International Conference on Machine Learning. ; 2020:778-787.' apa: Bengs, V., & Hüllermeier, E. (2020). Preselection Bandits. In International Conference on Machine Learning (pp. 778–787). 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} }' chicago: Bengs, Viktor, and Eyke Hüllermeier. “Preselection Bandits.” In International Conference on Machine Learning, 778–87, 2020. ieee: V. Bengs and E. Hüllermeier, “Preselection Bandits,” in International Conference on Machine Learning, 2020, pp. 778–787. mla: Bengs, Viktor, and Eyke Hüllermeier. “Preselection Bandits.” International Conference on Machine Learning, 2020, pp. 778–87. short: 'V. Bengs, E. Hüllermeier, in: International Conference on Machine Learning, 2020, pp. 778–787.' date_created: 2021-03-18T11:13:12Z date_updated: 2022-01-06T06:55:03Z department: - _id: '34' - _id: '7' - _id: '355' language: - iso: eng page: 778-787 project: - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: International Conference on Machine Learning status: public title: Preselection Bandits type: conference user_id: '76599' year: '2020' ... --- _id: '21536' abstract: - lang: eng text: "We consider a resource-aware variant of the classical multi-armed bandit\r\nproblem: In each round, the learner selects an arm and determines a resource\r\nlimit. It then observes a corresponding (random) reward, provided the (random)\r\namount of consumed resources remains below the limit. Otherwise, the\r\nobservation is censored, i.e., no reward is obtained. For this problem setting,\r\nwe introduce a measure of regret, which incorporates the actual amount of\r\nallocated resources of each learning round as well as the optimality of\r\nrealizable rewards. Thus, to minimize regret, the learner needs to set a\r\nresource limit and choose an arm in such a way that the chance to realize a\r\nhigh reward within the predefined resource limit is high, while the resource\r\nlimit itself should be kept as low as possible. We derive the theoretical lower\r\nbound on the cumulative regret and propose a learning algorithm having a regret\r\nupper bound that matches the lower bound. In a simulation study, we show that\r\nour learning algorithm outperforms straightforward extensions of standard\r\nmulti-armed bandit algorithms." author: - first_name: Viktor full_name: Bengs, Viktor last_name: Bengs - first_name: Eyke full_name: Hüllermeier, Eyke last_name: Hüllermeier citation: ama: Bengs V, Hüllermeier E. Multi-Armed Bandits with Censored Consumption of Resources. arXiv:201100813. 2020. apa: Bengs, V., & Hüllermeier, E. (2020). Multi-Armed Bandits with Censored Consumption of Resources. ArXiv:2011.00813. 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} }' chicago: Bengs, Viktor, and Eyke Hüllermeier. “Multi-Armed Bandits with Censored Consumption of Resources.” ArXiv:2011.00813, 2020. ieee: V. Bengs and E. Hüllermeier, “Multi-Armed Bandits with Censored Consumption of Resources,” arXiv:2011.00813. 2020. mla: Bengs, Viktor, and Eyke Hüllermeier. “Multi-Armed Bandits with Censored Consumption of Resources.” ArXiv:2011.00813, 2020. short: V. Bengs, E. Hüllermeier, ArXiv:2011.00813 (2020). date_created: 2021-03-18T11:27:37Z date_updated: 2022-01-06T06:55:03Z department: - _id: '34' - _id: '7' - _id: '355' language: - iso: eng project: - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: arXiv:2011.00813 status: public title: Multi-Armed Bandits with Censored Consumption of Resources type: preprint user_id: '76599' year: '2020' ... --- _id: '17407' author: - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - 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 citation: ama: 'Tornede A, Wever MD, Hüllermeier E. Extreme Algorithm Selection with Dyadic Feature Representation. In: Discovery Science. ; 2020.' apa: Tornede, A., Wever, M. D., & Hüllermeier, E. (2020). Extreme Algorithm Selection with Dyadic Feature Representation. Discovery Science. Discovery Science 2020. bibtex: '@inproceedings{Tornede_Wever_Hüllermeier_2020, title={Extreme Algorithm Selection with Dyadic Feature Representation}, booktitle={Discovery Science}, author={Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2020} }' chicago: Tornede, Alexander, Marcel Dominik Wever, and Eyke Hüllermeier. “Extreme Algorithm Selection with Dyadic Feature Representation.” 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. mla: Tornede, Alexander, et al. “Extreme Algorithm Selection with Dyadic Feature Representation.” Discovery Science, 2020. short: 'A. Tornede, M.D. Wever, E. Hüllermeier, in: Discovery Science, 2020.' conference: name: Discovery Science 2020 date_created: 2020-07-21T10:06:51Z date_updated: 2022-01-06T06:53:10Z department: - _id: '34' - _id: '355' - _id: '26' language: - iso: eng 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 publication: Discovery Science status: public title: Extreme Algorithm Selection with Dyadic Feature Representation type: conference user_id: '5786' year: '2020' ... --- _id: '17408' author: - first_name: Jonas Manuel full_name: Hanselle, Jonas Manuel id: '43980' last_name: Hanselle orcid: 0000-0002-1231-4985 - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - 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 citation: ama: 'Hanselle JM, Tornede A, Wever MD, Hüllermeier E. Hybrid Ranking and Regression for Algorithm Selection. In: KI 2020: Advances in Artificial Intelligence. ; 2020.' apa: 'Hanselle, J. M., Tornede, A., Wever, M. D., & Hüllermeier, E. (2020). Hybrid Ranking and Regression for Algorithm Selection. KI 2020: Advances in Artificial Intelligence. 43rd German Conference on Artificial Intelligence.' 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} }' chicago: 'Hanselle, Jonas Manuel, Alexander Tornede, Marcel Dominik Wever, and Eyke Hüllermeier. “Hybrid Ranking and Regression for Algorithm Selection.” In KI 2020: Advances in Artificial Intelligence, 2020.' 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. mla: 'Hanselle, Jonas Manuel, et al. “Hybrid Ranking and Regression for Algorithm Selection.” KI 2020: Advances in Artificial Intelligence, 2020.' short: 'J.M. Hanselle, A. Tornede, M.D. Wever, E. Hüllermeier, in: KI 2020: Advances in Artificial Intelligence, 2020.' conference: name: 43rd German Conference on Artificial Intelligence date_created: 2020-07-21T10:21:09Z date_updated: 2022-01-06T06:53:10Z department: - _id: '34' - _id: '355' - _id: '26' language: - iso: eng 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 publication: 'KI 2020: Advances in Artificial Intelligence' status: public title: Hybrid Ranking and Regression for Algorithm Selection type: conference user_id: '5786' year: '2020' ... --- _id: '17424' author: - first_name: Tanja full_name: Tornede, Tanja id: '40795' last_name: Tornede - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Tornede T, Tornede A, Wever MD, Mohr F, Hüllermeier E. AutoML for Predictive Maintenance: One Tool to RUL Them All. In: Proceedings of the ECMLPKDD 2020. ; 2020. doi:10.1007/978-3-030-66770-2_8' apa: 'Tornede, T., Tornede, A., Wever, M. D., Mohr, F., & Hüllermeier, E. (2020). AutoML for Predictive Maintenance: One Tool to RUL Them All. Proceedings of the ECMLPKDD 2020. IOTStream Workshop @ ECMLPKDD 2020. https://doi.org/10.1007/978-3-030-66770-2_8' bibtex: '@inproceedings{Tornede_Tornede_Wever_Mohr_Hüllermeier_2020, title={AutoML for Predictive Maintenance: One Tool to RUL Them All}, DOI={10.1007/978-3-030-66770-2_8}, booktitle={Proceedings of the ECMLPKDD 2020}, author={Tornede, Tanja and Tornede, Alexander and Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2020} }' chicago: 'Tornede, Tanja, Alexander Tornede, Marcel Dominik Wever, Felix Mohr, and Eyke Hüllermeier. “AutoML for Predictive Maintenance: One Tool to RUL Them All.” In Proceedings of the ECMLPKDD 2020, 2020. https://doi.org/10.1007/978-3-030-66770-2_8.' ieee: 'T. Tornede, A. Tornede, M. D. Wever, F. Mohr, and E. Hüllermeier, “AutoML for Predictive Maintenance: One Tool to RUL Them All,” presented at the IOTStream Workshop @ ECMLPKDD 2020, 2020, doi: 10.1007/978-3-030-66770-2_8.' mla: 'Tornede, Tanja, et al. “AutoML for Predictive Maintenance: One Tool to RUL Them All.” Proceedings of the ECMLPKDD 2020, 2020, doi:10.1007/978-3-030-66770-2_8.' short: 'T. Tornede, A. Tornede, M.D. Wever, F. Mohr, E. Hüllermeier, in: Proceedings of the ECMLPKDD 2020, 2020.' conference: name: IOTStream Workshop @ ECMLPKDD 2020 date_created: 2020-07-28T09:17:41Z date_updated: 2022-01-06T06:53:11Z department: - _id: '34' - _id: '355' - _id: '26' doi: 10.1007/978-3-030-66770-2_8 language: - iso: eng project: - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 - _id: '1' name: SFB 901 - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: Proceedings of the ECMLPKDD 2020 status: public title: 'AutoML for Predictive Maintenance: One Tool to RUL Them All' type: conference user_id: '5786' year: '2020' ...