--- _id: '45780' author: - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede citation: ama: 'Tornede A. Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions.; 2023. doi:10.17619/UNIPB/1-1780 ' apa: 'Tornede, A. (2023). Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions. https://doi.org/10.17619/UNIPB/1-1780 ' bibtex: '@book{Tornede_2023, title={Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions}, DOI={10.17619/UNIPB/1-1780 }, author={Tornede, Alexander}, year={2023} }' chicago: 'Tornede, Alexander. Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions, 2023. https://doi.org/10.17619/UNIPB/1-1780 .' ieee: 'A. Tornede, Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions. 2023.' mla: 'Tornede, Alexander. Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions. 2023, doi:10.17619/UNIPB/1-1780 .' short: 'A. Tornede, Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions, 2023.' date_created: 2023-06-27T05:20:14Z date_updated: 2023-08-04T06:01:49Z ddc: - '006' department: - _id: '355' doi: '10.17619/UNIPB/1-1780 ' file: - access_level: open_access content_type: application/pdf creator: ahetzer date_created: 2023-07-24T08:40:35Z date_updated: 2023-07-24T08:42:01Z file_id: '46118' file_name: dissertation_alexander_tornede_final_publishing_compressed.pdf file_size: 4300633 relation: main_file title: ' Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions' file_date_updated: 2023-07-24T08:42:01Z has_accepted_license: '1' language: - iso: eng oa: '1' project: - _id: '10' grant_number: '160364472' name: 'SFB 901 - B2: Konfiguration und Bewertung (B02)' - _id: '3' name: 'SFB 901 - B: SFB 901 - Project Area B' - _id: '1' grant_number: '160364472' name: 'SFB 901: SFB 901: On-The-Fly Computing - Individualisierte IT-Dienstleistungen in dynamischen Märkten ' status: public supervisor: - first_name: Eyke full_name: Hüllermeier, Eyke last_name: Hüllermeier title: 'Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions' type: dissertation user_id: '15504' year: '2023' ... --- _id: '21600' abstract: - lang: eng text: Many problems in science and engineering require an efficient numerical approximation of integrals or solutions to differential equations. For systems with rapidly changing dynamics, an equidistant discretization is often inadvisable as it results in prohibitively large errors or computational effort. To this end, adaptive schemes, such as solvers based on Runge–Kutta pairs, have been developed which adapt the step size based on local error estimations at each step. While the classical schemes apply very generally and are highly efficient on regular systems, they can behave suboptimally when an inefficient step rejection mechanism is triggered by structurally complex systems such as chaotic systems. To overcome these issues, we propose a method to tailor numerical schemes to the problem class at hand. This is achieved by combining simple, classical quadrature rules or ODE solvers with data-driven time-stepping controllers. Compared with learning solution operators to ODEs directly, it generalizes better to unseen initial data as our approach employs classical numerical schemes as base methods. At the same time it can make use of identified structures of a problem class and, therefore, outperforms state-of-the-art adaptive schemes. Several examples demonstrate superior efficiency. Source code is available at https://github.com/lueckem/quadrature-ML. author: - first_name: Michael full_name: Dellnitz, Michael last_name: Dellnitz - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier - first_name: Marvin full_name: Lücke, Marvin last_name: Lücke - first_name: Sina full_name: Ober-Blöbaum, Sina id: '16494' last_name: Ober-Blöbaum - first_name: Christian full_name: Offen, Christian id: '85279' last_name: Offen orcid: 0000-0002-5940-8057 - first_name: Sebastian full_name: Peitz, Sebastian id: '47427' last_name: Peitz orcid: 0000-0002-3389-793X - first_name: Karlson full_name: Pfannschmidt, Karlson id: '13472' last_name: Pfannschmidt orcid: 0000-0001-9407-7903 citation: ama: Dellnitz M, Hüllermeier E, Lücke M, et al. Efficient time stepping for numerical integration using reinforcement  learning. SIAM Journal on Scientific Computing. 2023;45(2):A579-A595. doi:10.1137/21M1412682 apa: Dellnitz, M., Hüllermeier, E., Lücke, M., Ober-Blöbaum, S., Offen, C., Peitz, S., & Pfannschmidt, K. (2023). Efficient time stepping for numerical integration using reinforcement  learning. SIAM Journal on Scientific Computing, 45(2), A579–A595. https://doi.org/10.1137/21M1412682 bibtex: '@article{Dellnitz_Hüllermeier_Lücke_Ober-Blöbaum_Offen_Peitz_Pfannschmidt_2023, title={Efficient time stepping for numerical integration using reinforcement  learning}, volume={45}, DOI={10.1137/21M1412682}, number={2}, journal={SIAM Journal on Scientific Computing}, author={Dellnitz, Michael and Hüllermeier, Eyke and Lücke, Marvin and Ober-Blöbaum, Sina and Offen, Christian and Peitz, Sebastian and Pfannschmidt, Karlson}, year={2023}, pages={A579–A595} }' chicago: 'Dellnitz, Michael, Eyke Hüllermeier, Marvin Lücke, Sina Ober-Blöbaum, Christian Offen, Sebastian Peitz, and Karlson Pfannschmidt. “Efficient Time Stepping for Numerical Integration Using Reinforcement  Learning.” SIAM Journal on Scientific Computing 45, no. 2 (2023): A579–95. https://doi.org/10.1137/21M1412682.' ieee: 'M. Dellnitz et al., “Efficient time stepping for numerical integration using reinforcement  learning,” SIAM Journal on Scientific Computing, vol. 45, no. 2, pp. A579–A595, 2023, doi: 10.1137/21M1412682.' mla: Dellnitz, Michael, et al. “Efficient Time Stepping for Numerical Integration Using Reinforcement  Learning.” SIAM Journal on Scientific Computing, vol. 45, no. 2, 2023, pp. A579–95, doi:10.1137/21M1412682. short: M. Dellnitz, E. Hüllermeier, M. Lücke, S. Ober-Blöbaum, C. Offen, S. Peitz, K. Pfannschmidt, SIAM Journal on Scientific Computing 45 (2023) A579–A595. date_created: 2021-04-09T07:59:19Z date_updated: 2023-08-25T09:24:50Z ddc: - '510' department: - _id: '101' - _id: '636' - _id: '355' - _id: '655' doi: 10.1137/21M1412682 external_id: arxiv: - arXiv:2104.03562 has_accepted_license: '1' intvolume: ' 45' issue: '2' language: - iso: eng main_file_link: - url: https://epubs.siam.org/doi/reader/10.1137/21M1412682 page: A579-A595 publication: SIAM Journal on Scientific Computing publication_status: published related_material: link: - description: GitHub relation: software url: https://github.com/lueckem/quadrature-ML status: public title: Efficient time stepping for numerical integration using reinforcement learning type: journal_article user_id: '47427' volume: 45 year: '2023' ... --- _id: '24382' author: - first_name: Karina full_name: Gevers, Karina id: '83151' last_name: Gevers - first_name: Volker full_name: Schöppner, Volker id: '20530' last_name: Schöppner - first_name: Eyke full_name: Hüllermeier, Eyke last_name: Hüllermeier citation: ama: 'Gevers K, Schöppner V, Hüllermeier E. Heated tool butt welding of two different materials –  Established methods versus artificial intelligence. In: ; 2021.' apa: Gevers, K., Schöppner, V., & Hüllermeier, E. (2021). Heated tool butt welding of two different materials –  Established methods versus artificial intelligence. International Institute of Welding, online. bibtex: '@inproceedings{Gevers_Schöppner_Hüllermeier_2021, title={Heated tool butt welding of two different materials –  Established methods versus artificial intelligence}, author={Gevers, Karina and Schöppner, Volker and Hüllermeier, Eyke}, year={2021} }' chicago: Gevers, Karina, Volker Schöppner, and Eyke Hüllermeier. “Heated Tool Butt Welding of Two Different Materials –  Established Methods versus Artificial Intelligence,” 2021. ieee: K. Gevers, V. Schöppner, and E. Hüllermeier, “Heated tool butt welding of two different materials –  Established methods versus artificial intelligence,” presented at the International Institute of Welding, online, 2021. mla: Gevers, Karina, et al. Heated Tool Butt Welding of Two Different Materials –  Established Methods versus Artificial Intelligence. 2021. short: 'K. Gevers, V. Schöppner, E. Hüllermeier, in: 2021.' conference: end_date: 2021-07-14 location: online name: International Institute of Welding start_date: 2021-07-12 date_created: 2021-09-14T11:34:31Z date_updated: 2022-01-06T06:56:19Z department: - _id: '367' - _id: '355' - _id: '321' language: - iso: eng status: public title: Heated tool butt welding of two different materials – Established methods versus artificial intelligence type: conference user_id: '83151' year: '2021' ... --- _id: '21004' abstract: - lang: eng text: 'Automated machine learning (AutoML) supports the algorithmic construction and data-specific customization of machine learning pipelines, including the selection, combination, and parametrization of machine learning algorithms as main constituents. Generally speaking, AutoML approaches comprise two major components: a search space model and an optimizer for traversing the space. Recent approaches have shown impressive results in the realm of supervised learning, most notably (single-label) classification (SLC). Moreover, first attempts at extending these approaches towards multi-label classification (MLC) have been made. While the space of candidate pipelines is already huge in SLC, the complexity of the search space is raised to an even higher power in MLC. One may wonder, therefore, whether and to what extent optimizers established for SLC can scale to this increased complexity, and how they compare to each other. This paper makes the following contributions: First, we survey existing approaches to AutoML for MLC. Second, we augment these approaches with optimizers not previously tried for MLC. Third, we propose a benchmarking framework that supports a fair and systematic comparison. Fourth, we conduct an extensive experimental study, evaluating the methods on a suite of MLC problems. We find a grammar-based best-first search to compare favorably to other optimizers.' author: - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Wever MD, Tornede A, Mohr F, Hüllermeier E. AutoML for Multi-Label Classification: Overview and Empirical Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence. Published online 2021:1-1. doi:10.1109/tpami.2021.3051276' apa: 'Wever, M. D., Tornede, A., Mohr, F., & Hüllermeier, E. (2021). AutoML for Multi-Label Classification: Overview and Empirical Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/tpami.2021.3051276' bibtex: '@article{Wever_Tornede_Mohr_Hüllermeier_2021, title={AutoML for Multi-Label Classification: Overview and Empirical Evaluation}, DOI={10.1109/tpami.2021.3051276}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, author={Wever, Marcel Dominik and Tornede, Alexander and Mohr, Felix and Hüllermeier, Eyke}, year={2021}, pages={1–1} }' chicago: 'Wever, Marcel Dominik, Alexander Tornede, Felix Mohr, and Eyke Hüllermeier. “AutoML for Multi-Label Classification: Overview and Empirical Evaluation.” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 1–1. https://doi.org/10.1109/tpami.2021.3051276.' ieee: 'M. D. Wever, A. Tornede, F. Mohr, and E. Hüllermeier, “AutoML for Multi-Label Classification: Overview and Empirical Evaluation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1–1, 2021, doi: 10.1109/tpami.2021.3051276.' mla: 'Wever, Marcel Dominik, et al. “AutoML for Multi-Label Classification: Overview and Empirical Evaluation.” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, pp. 1–1, doi:10.1109/tpami.2021.3051276.' short: M.D. Wever, A. Tornede, F. Mohr, E. Hüllermeier, IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) 1–1. date_created: 2021-01-16T14:48:13Z date_updated: 2022-01-06T06:54:42Z department: - _id: '34' - _id: '355' - _id: '26' doi: 10.1109/tpami.2021.3051276 keyword: - Automated Machine Learning - Multi Label Classification - Hierarchical Planning - Bayesian Optimization language: - iso: eng page: 1-1 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: IEEE Transactions on Pattern Analysis and Machine Intelligence publication_identifier: issn: - 0162-8828 - 2160-9292 - 1939-3539 publication_status: published status: public title: 'AutoML for Multi-Label Classification: Overview and Empirical Evaluation' type: journal_article user_id: '5786' year: '2021' ... --- _id: '21092' abstract: - lang: eng text: "Automated Machine Learning (AutoML) seeks to automatically find so-called machine learning pipelines that maximize the prediction performance when being used to train a model on a given dataset. One of the main and yet open challenges in AutoML is an effective use of computational resources: An AutoML process involves the evaluation of many candidate pipelines, which are costly but often ineffective because they are canceled due to a timeout.\r\nIn this paper, we present an approach to predict the runtime of two-step machine learning pipelines with up to one pre-processor, which can be used to anticipate whether or not a pipeline will time out. Separate runtime models are trained offline for each algorithm that may be used in a pipeline, and an overall prediction is derived from these models. We empirically show that the approach increases successful evaluations made by an AutoML tool while preserving or even improving on the previously best solutions." author: - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: Mohr F, Wever MD, Tornede A, Hüllermeier E. Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence. apa: Mohr, F., Wever, M. D., Tornede, A., & Hüllermeier, E. (n.d.). Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence. bibtex: '@article{Mohr_Wever_Tornede_Hüllermeier, title={Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, publisher={IEEE}, author={Mohr, Felix and Wever, Marcel Dominik and Tornede, Alexander and Hüllermeier, Eyke} }' chicago: Mohr, Felix, Marcel Dominik Wever, Alexander Tornede, and Eyke Hüllermeier. “Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning.” IEEE Transactions on Pattern Analysis and Machine Intelligence, n.d. ieee: F. Mohr, M. D. Wever, A. Tornede, and E. Hüllermeier, “Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence. mla: Mohr, Felix, et al. “Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning.” IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE. short: F. Mohr, M.D. Wever, A. Tornede, E. Hüllermeier, IEEE Transactions on Pattern Analysis and Machine Intelligence (n.d.). date_created: 2021-01-27T13:45:52Z date_updated: 2022-01-06T06:54:45Z 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: IEEE Transactions on Pattern Analysis and Machine Intelligence publication_status: accepted publisher: IEEE status: public title: Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning type: journal_article user_id: '5786' year: '2021' ... --- _id: '21535' author: - first_name: Viktor full_name: Bengs, Viktor last_name: Bengs - first_name: Róbert full_name: Busa-Fekete, Róbert last_name: Busa-Fekete - first_name: Adil full_name: El Mesaoudi-Paul, Adil last_name: El Mesaoudi-Paul - first_name: Eyke full_name: Hüllermeier, Eyke last_name: Hüllermeier citation: ama: 'Bengs V, Busa-Fekete R, El Mesaoudi-Paul A, Hüllermeier E. Preference-based Online Learning with Dueling Bandits: A Survey. Journal of Machine Learning Research. 2021;22(7):1-108.' apa: 'Bengs, V., Busa-Fekete, R., El Mesaoudi-Paul, A., & Hüllermeier, E. (2021). Preference-based Online Learning with Dueling Bandits: A Survey. Journal of Machine Learning Research, 22(7), 1–108.' bibtex: '@article{Bengs_Busa-Fekete_El Mesaoudi-Paul_Hüllermeier_2021, title={Preference-based Online Learning with Dueling Bandits: A Survey}, volume={22}, number={7}, journal={Journal of Machine Learning Research}, author={Bengs, Viktor and Busa-Fekete, Róbert and El Mesaoudi-Paul, Adil and Hüllermeier, Eyke}, year={2021}, pages={1–108} }' chicago: 'Bengs, Viktor, Róbert Busa-Fekete, Adil El Mesaoudi-Paul, and Eyke Hüllermeier. “Preference-Based Online Learning with Dueling Bandits: A Survey.” Journal of Machine Learning Research 22, no. 7 (2021): 1–108.' ieee: 'V. Bengs, R. Busa-Fekete, A. El Mesaoudi-Paul, and E. Hüllermeier, “Preference-based Online Learning with Dueling Bandits: A Survey,” Journal of Machine Learning Research, vol. 22, no. 7, pp. 1–108, 2021.' mla: 'Bengs, Viktor, et al. “Preference-Based Online Learning with Dueling Bandits: A Survey.” Journal of Machine Learning Research, vol. 22, no. 7, 2021, pp. 1–108.' short: V. Bengs, R. Busa-Fekete, A. El Mesaoudi-Paul, E. Hüllermeier, Journal of Machine Learning Research 22 (2021) 1–108. date_created: 2021-03-18T11:15:38Z date_updated: 2022-01-06T06:55:03Z department: - _id: '34' - _id: '7' - _id: '355' intvolume: ' 22' issue: '7' language: - iso: eng page: 1-108 publication: Journal of Machine Learning Research quality_controlled: '1' status: public title: 'Preference-based Online Learning with Dueling Bandits: A Survey' type: journal_article user_id: '76599' volume: 22 year: '2021' ... --- _id: '21570' 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: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Tornede T, Tornede A, Wever MD, Hüllermeier E. Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance. In: Proceedings of the Genetic and Evolutionary Computation Conference. ; 2021.' apa: Tornede, T., Tornede, A., Wever, M. D., & Hüllermeier, E. (2021). Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance. Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference. bibtex: '@inproceedings{Tornede_Tornede_Wever_Hüllermeier_2021, title={Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, author={Tornede, Tanja and Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2021} }' chicago: Tornede, Tanja, Alexander Tornede, Marcel Dominik Wever, and Eyke Hüllermeier. “Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance.” In Proceedings of the Genetic and Evolutionary Computation Conference, 2021. ieee: T. Tornede, A. Tornede, M. D. Wever, and E. Hüllermeier, “Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance,” presented at the Genetic and Evolutionary Computation Conference, 2021. mla: Tornede, Tanja, et al. “Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance.” Proceedings of the Genetic and Evolutionary Computation Conference, 2021. short: 'T. Tornede, A. Tornede, M.D. Wever, E. Hüllermeier, in: Proceedings of the Genetic and Evolutionary Computation Conference, 2021.' conference: end_date: 2021-07-14 name: Genetic and Evolutionary Computation Conference start_date: 2021-07-10 date_created: 2021-03-26T09:14:19Z date_updated: 2022-01-06T06:55:06Z 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 publication: Proceedings of the Genetic and Evolutionary Computation Conference status: public title: Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance type: conference user_id: '5786' year: '2021' ... --- _id: '23779' abstract: - lang: ger text: "Produktentstehung (PE) bezieht sich auf den Prozess der Planung und Entwicklung eines Produkts sowie der damit verbundenen Dienstleistungen von der ersten Idee bis zur Herstellung und zum Vertrieb. Während dieses Prozesses gibt es zahlreiche Aufgaben, die von menschlichem Fachwissen abhängen und typischerweise von erfahrenen Experten übernommen werden. Da sich das Feld der Künstlichen Intelligenz (KI) immer weiterentwickelt und seinen Weg in den Fertigungssektor findet, gibt es viele Möglichkeiten für eine Anwendung von KI, um bei der Lösung der oben genannten Aufgaben zu helfen. In diesem Paper geben wir einen umfassenden Überblick über den aktuellen Stand der Technik des Einsatzes von KI in der PE. \r\nIm Detail analysieren wir 40 bestehende Surveys zu KI in der PE und 94 Case Studies, um herauszufinden, welche Bereiche der PE von der aktuellen Forschung in diesem Bereich vorrangig adressiert werden, wie ausgereift die diskutierten KI-Methoden sind und inwieweit datenzentrierte Ansätze in der aktuellen Forschung genutzt werden." - lang: eng text: "Product Creation (PC) refers to the process of planning and developing a product as well as related services from the initial idea until manufacturing and distribution. Throughout this process, there are numerous tasks that depend on human expertise and are typically undertaken by experienced practitioners. As the field of Artificial Intelligence (AI) continues to evolve and finds its way into the manufacturing sector, there exist many possibilities for an application of AI in order to assist in solving aforementioned tasks. In this work, we provide a comprehensive overview of the current state of the art of the use of AI in PC. \r\nIn detail, we analyze 40 existing surveys on AI in PC and 94 case studies in order to find out which areas of PC are primarily addressed by current research in this field, how mature the discussed AI methods are, and to which extent data-centric approaches are utilized in current research." author: - first_name: Ruslan full_name: Bernijazov, Ruslan last_name: Bernijazov - first_name: Alexander full_name: Dicks, Alexander last_name: Dicks - first_name: Roman full_name: Dumitrescu, Roman id: '16190' last_name: Dumitrescu - first_name: Marc full_name: Foullois, Marc last_name: Foullois - first_name: Jonas Manuel full_name: Hanselle, Jonas Manuel id: '43980' last_name: Hanselle orcid: 0000-0002-1231-4985 - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier - first_name: Gökce full_name: Karakaya, Gökce last_name: Karakaya - first_name: Patrick full_name: Ködding, Patrick id: '45402' last_name: Ködding - first_name: Volker full_name: Lohweg, Volker last_name: Lohweg - first_name: Manuel full_name: Malatyali, Manuel id: '41265' last_name: Malatyali - first_name: Friedhelm full_name: Meyer auf der Heide, Friedhelm id: '15523' last_name: Meyer auf der Heide - first_name: Melina full_name: Panzner, Melina last_name: Panzner - first_name: Christian full_name: Soltenborn, Christian id: '1737' last_name: Soltenborn orcid: 0000-0002-0342-8227 citation: ama: 'Bernijazov R, Dicks A, Dumitrescu R, et al. A Meta-Review on Artificial Intelligence in Product Creation. In: Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21). ; 2021.' apa: Bernijazov, R., Dicks, A., Dumitrescu, R., Foullois, M., Hanselle, J. M., Hüllermeier, E., Karakaya, G., Ködding, P., Lohweg, V., Malatyali, M., Meyer auf der Heide, F., Panzner, M., & Soltenborn, C. (2021). A Meta-Review on Artificial Intelligence in Product Creation. Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21). 30th International Joint Conference on Artificial Intelligence (IJCAI 2021) - Workshop “AI and Product Design,” Montreal, Kanada. 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} }' chicago: Bernijazov, Ruslan, Alexander Dicks, Roman Dumitrescu, Marc Foullois, Jonas Manuel Hanselle, Eyke Hüllermeier, Gökce Karakaya, et al. “A Meta-Review on Artificial Intelligence in Product Creation.” In Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21), 2021. ieee: R. Bernijazov et al., “A Meta-Review on Artificial Intelligence in Product Creation,” presented at the 30th International Joint Conference on Artificial Intelligence (IJCAI 2021) - Workshop “AI and Product Design,” Montreal, Kanada, 2021. mla: Bernijazov, Ruslan, et al. “A Meta-Review on Artificial Intelligence in Product Creation.” Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21), 2021. 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.' conference: end_date: 2021-08-26 location: Montreal, Kanada name: 30th International Joint Conference on Artificial Intelligence (IJCAI 2021) - Workshop "AI and Product Design" start_date: 2021-08-19 date_created: 2021-09-06T08:23:45Z date_updated: 2022-01-06T06:55:59Z department: - _id: '63' - _id: '563' - _id: '355' - _id: '241' keyword: - Artificial Intelligence Product Creation Literature Review language: - iso: eng 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 publication: Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21) publication_status: epub_ahead quality_controlled: '1' status: public title: A Meta-Review on Artificial Intelligence in Product Creation type: conference user_id: '15415' year: '2021' ... --- _id: '22913' author: - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier - first_name: Felix full_name: Mohr, Felix last_name: Mohr - 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' citation: ama: 'Hüllermeier E, Mohr F, Tornede A, Wever MD. Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. In: ; 2021.' apa: Hüllermeier, E., Mohr, F., Tornede, A., & Wever, M. D. (2021). Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. ECML/PKDD Workshop on Automating Data Science, Bilbao (Virtual). bibtex: '@inproceedings{Hüllermeier_Mohr_Tornede_Wever_2021, title={Automated Machine Learning, Bounded Rationality, and Rational Metareasoning}, author={Hüllermeier, Eyke and Mohr, Felix and Tornede, Alexander and Wever, Marcel Dominik}, year={2021} }' chicago: Hüllermeier, Eyke, Felix Mohr, Alexander Tornede, and Marcel Dominik Wever. “Automated Machine Learning, Bounded Rationality, and Rational Metareasoning,” 2021. ieee: E. Hüllermeier, F. Mohr, A. Tornede, and M. D. Wever, “Automated Machine Learning, Bounded Rationality, and Rational Metareasoning,” presented at the ECML/PKDD Workshop on Automating Data Science, Bilbao (Virtual), 2021. mla: Hüllermeier, Eyke, et al. Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. 2021. short: 'E. Hüllermeier, F. Mohr, A. Tornede, M.D. Wever, in: 2021.' conference: end_date: 2021-09-17 location: Bilbao (Virtual) name: ECML/PKDD Workshop on Automating Data Science start_date: 2021-09-13 date_created: 2021-08-02T07:46:29Z date_updated: 2022-01-06T06:55:43Z 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 quality_controlled: '1' status: public title: Automated Machine Learning, Bounded Rationality, and Rational Metareasoning type: conference user_id: '5786' year: '2021' ... --- _id: '22914' author: - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' citation: ama: 'Mohr F, Wever MD. Replacing the Ex-Def Baseline in AutoML by Naive AutoML. In: ; 2021.' apa: Mohr, F., & Wever, M. D. (2021). Replacing the Ex-Def Baseline in AutoML by Naive AutoML. 8th ICML Workshop on Automated Machine Learning, Virtual. bibtex: '@inproceedings{Mohr_Wever_2021, title={Replacing the Ex-Def Baseline in AutoML by Naive AutoML}, author={Mohr, Felix and Wever, Marcel Dominik}, year={2021} }' chicago: Mohr, Felix, and Marcel Dominik Wever. “Replacing the Ex-Def Baseline in AutoML by Naive AutoML,” 2021. ieee: F. Mohr and M. D. Wever, “Replacing the Ex-Def Baseline in AutoML by Naive AutoML,” presented at the 8th ICML Workshop on Automated Machine Learning, Virtual, 2021. mla: Mohr, Felix, and Marcel Dominik Wever. Replacing the Ex-Def Baseline in AutoML by Naive AutoML. 2021. short: 'F. Mohr, M.D. Wever, in: 2021.' conference: end_date: 2021-07-23 location: Virtual name: 8th ICML Workshop on Automated Machine Learning start_date: 2021-07-23 date_created: 2021-08-02T07:48:07Z date_updated: 2022-01-06T06:55:43Z department: - _id: '34' - _id: '355' - _id: '26' language: - iso: eng status: public title: Replacing the Ex-Def Baseline in AutoML by Naive AutoML type: conference user_id: '5786' year: '2021' ... --- _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' ... --- _id: '17605' 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." author: - first_name: Stefan Helmut full_name: Heid, Stefan Helmut id: '39640' last_name: Heid orcid: 0000-0002-9461-7372 - 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: Heid SH, Wever MD, Hüllermeier E. Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction. Journal of Data Mining and Digital Humanities. apa: Heid, S. H., Wever, M. D., & Hüllermeier, E. (n.d.). Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction. In Journal of Data Mining and Digital Humanities. episciences. 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} }' chicago: Heid, Stefan Helmut, Marcel Dominik Wever, and Eyke Hüllermeier. “Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction.” Journal of Data Mining and Digital Humanities. episciences, n.d. ieee: S. H. Heid, M. D. Wever, and E. Hüllermeier, “Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction,” Journal of Data Mining and Digital Humanities. episciences. mla: Heid, Stefan Helmut, et al. “Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction.” Journal of Data Mining and Digital Humanities, episciences. short: S.H. Heid, M.D. Wever, E. Hüllermeier, Journal of Data Mining and Digital Humanities (n.d.). date_created: 2020-08-05T06:52:53Z date_updated: 2022-01-06T06:53:15Z department: - _id: '34' - _id: '355' - _id: '26' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2008.01377 oa: '1' project: - _id: '39' name: InterGramm publication: Journal of Data Mining and Digital Humanities publication_status: submitted publisher: episciences status: public title: Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction type: preprint user_id: '5786' year: '2020' ... --- _id: '20306' 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. Towards Meta-Algorithm Selection. In: Workshop MetaLearn 2020 @ NeurIPS 2020. ; 2020.' apa: Tornede, A., Wever, M. D., & Hüllermeier, E. (2020). Towards Meta-Algorithm Selection. Workshop MetaLearn 2020 @ NeurIPS 2020. Workshop MetaLearn 2020 @ NeurIPS 2020, Online. 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} }' chicago: Tornede, Alexander, Marcel Dominik Wever, and Eyke Hüllermeier. “Towards Meta-Algorithm Selection.” In Workshop MetaLearn 2020 @ NeurIPS 2020, 2020. ieee: A. Tornede, M. D. Wever, and E. Hüllermeier, “Towards Meta-Algorithm Selection,” presented at the Workshop MetaLearn 2020 @ NeurIPS 2020, Online, 2020. mla: Tornede, Alexander, et al. “Towards Meta-Algorithm Selection.” Workshop MetaLearn 2020 @ NeurIPS 2020, 2020. short: 'A. Tornede, M.D. Wever, E. Hüllermeier, in: Workshop MetaLearn 2020 @ NeurIPS 2020, 2020.' conference: location: Online name: Workshop MetaLearn 2020 @ NeurIPS 2020 date_created: 2020-11-06T09:42:27Z date_updated: 2022-01-06T06:54:26Z 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: Workshop MetaLearn 2020 @ NeurIPS 2020 status: public title: Towards Meta-Algorithm Selection type: conference user_id: '5786' year: '2020' ... --- _id: '18014' author: - first_name: Adil full_name: El Mesaoudi-Paul, Adil last_name: El Mesaoudi-Paul - first_name: Dimitri full_name: Weiß, Dimitri last_name: Weiß - first_name: Viktor full_name: Bengs, Viktor id: '76599' last_name: Bengs - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier - first_name: Kevin full_name: Tierney, Kevin last_name: Tierney citation: ama: 'El Mesaoudi-Paul A, Weiß D, Bengs V, Hüllermeier E, Tierney K. Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach. In: Learning and Intelligent Optimization. LION 2020. Vol 12096. Lecture Notes in Computer Science. Cham: Springer; 2020:216-232. doi:10.1007/978-3-030-53552-0_22' apa: 'El Mesaoudi-Paul, A., Weiß, D., Bengs, V., Hüllermeier, E., & Tierney, K. (2020). Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach. In Learning and Intelligent Optimization. LION 2020. (Vol. 12096, pp. 216–232). Cham: Springer. https://doi.org/10.1007/978-3-030-53552-0_22' bibtex: '@inbook{El Mesaoudi-Paul_Weiß_Bengs_Hüllermeier_Tierney_2020, place={Cham}, series={Lecture Notes in Computer Science}, title={Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach}, volume={12096}, DOI={10.1007/978-3-030-53552-0_22}, booktitle={Learning and Intelligent Optimization. LION 2020.}, publisher={Springer}, author={El Mesaoudi-Paul, Adil and Weiß, Dimitri and Bengs, Viktor and Hüllermeier, Eyke and Tierney, Kevin}, year={2020}, pages={216–232}, collection={Lecture Notes in Computer Science} }' chicago: 'El Mesaoudi-Paul, Adil, Dimitri Weiß, Viktor Bengs, Eyke Hüllermeier, and Kevin Tierney. “Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach.” In Learning and Intelligent Optimization. LION 2020., 12096:216–32. Lecture Notes in Computer Science. Cham: Springer, 2020. https://doi.org/10.1007/978-3-030-53552-0_22.' ieee: 'A. El Mesaoudi-Paul, D. Weiß, V. Bengs, E. Hüllermeier, and K. Tierney, “Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach,” in Learning and Intelligent Optimization. LION 2020., vol. 12096, Cham: Springer, 2020, pp. 216–232.' mla: 'El Mesaoudi-Paul, Adil, et al. “Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach.” Learning and Intelligent Optimization. LION 2020., vol. 12096, Springer, 2020, pp. 216–32, doi:10.1007/978-3-030-53552-0_22.' 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.' date_created: 2020-08-17T11:44:37Z date_updated: 2022-01-06T06:53:25Z department: - _id: '34' - _id: '7' - _id: '355' doi: 10.1007/978-3-030-53552-0_22 intvolume: ' 12096' language: - iso: eng page: 216 - 232 place: Cham project: - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: Learning and Intelligent Optimization. LION 2020. publication_identifier: isbn: - '9783030535513' - '9783030535520' issn: - 0302-9743 - 1611-3349 publication_status: published publisher: Springer series_title: Lecture Notes in Computer Science status: public title: 'Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach' type: book_chapter user_id: '76599' volume: 12096 year: '2020' ... --- _id: '18017' abstract: - lang: eng text: "We consider an extension of the contextual multi-armed bandit problem, in\r\nwhich, instead of selecting a single alternative (arm), a learner is supposed\r\nto make a preselection in the form of a subset of alternatives. More\r\nspecifically, in each iteration, the learner is presented a set of arms and a\r\ncontext, both described in terms of feature vectors. The task of the learner is\r\nto preselect $k$ of these arms, among which a final choice is made in a second\r\nstep. In our setup, we assume that each arm has a latent (context-dependent)\r\nutility, and that feedback on a preselection is produced according to a\r\nPlackett-Luce model. We propose the CPPL algorithm, which is inspired by the\r\nwell-known UCB algorithm, and evaluate this algorithm on synthetic and real\r\ndata. In particular, we consider an online algorithm selection scenario, which\r\nserved as a main motivation of our problem setting. Here, an instance (which\r\ndefines the context) from a certain problem class (such as SAT) can be solved\r\nby different algorithms (the arms), but only $k$ of these algorithms can\r\nactually be run." author: - first_name: Adil full_name: El Mesaoudi-Paul, Adil last_name: El Mesaoudi-Paul - first_name: Viktor full_name: Bengs, Viktor id: '76599' last_name: Bengs - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: El Mesaoudi-Paul A, Bengs V, Hüllermeier E. Online Preselection with Context Information under the Plackett-Luce  Model. arXiv:200204275. apa: El Mesaoudi-Paul, A., Bengs, V., & Hüllermeier, E. (n.d.). Online Preselection with Context Information under the Plackett-Luce  Model. ArXiv:2002.04275. bibtex: '@article{El Mesaoudi-Paul_Bengs_Hüllermeier, title={Online Preselection with Context Information under the Plackett-Luce  Model}, journal={arXiv:2002.04275}, author={El Mesaoudi-Paul, Adil and Bengs, Viktor and Hüllermeier, Eyke} }' chicago: El Mesaoudi-Paul, Adil, Viktor Bengs, and Eyke Hüllermeier. “Online Preselection with Context Information under the Plackett-Luce  Model.” ArXiv:2002.04275, n.d. ieee: A. El Mesaoudi-Paul, V. Bengs, and E. Hüllermeier, “Online Preselection with Context Information under the Plackett-Luce  Model,” arXiv:2002.04275. . mla: El Mesaoudi-Paul, Adil, et al. “Online Preselection with Context Information under the Plackett-Luce  Model.” ArXiv:2002.04275. short: A. El Mesaoudi-Paul, V. Bengs, E. Hüllermeier, ArXiv:2002.04275 (n.d.). date_created: 2020-08-17T11:49:40Z date_updated: 2022-01-06T06:53:25Z 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:2002.04275 publication_status: draft status: public title: Online Preselection with Context Information under the Plackett-Luce Model type: preprint user_id: '76599' year: '2020' ... --- _id: '18276' abstract: - lang: eng 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." 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: Stefan full_name: Werner, Stefan last_name: Werner - 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 A, Wever MD, Werner S, Mohr F, Hüllermeier E. Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. In: ACML 2020. ; 2020.' apa: 'Tornede, A., Wever, M. D., Werner, S., Mohr, F., & Hüllermeier, E. (2020). Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. ACML 2020. 12th Asian Conference on Machine Learning, Bangkok, Thailand.' 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} }' chicago: 'Tornede, Alexander, Marcel Dominik Wever, Stefan Werner, Felix Mohr, and Eyke Hüllermeier. “Run2Survive: A Decision-Theoretic Approach to Algorithm Selection Based on Survival Analysis.” In ACML 2020, 2020.' 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.' mla: 'Tornede, Alexander, et al. “Run2Survive: A Decision-Theoretic Approach to Algorithm Selection Based on Survival Analysis.” ACML 2020, 2020.' short: 'A. Tornede, M.D. Wever, S. Werner, F. Mohr, E. Hüllermeier, in: ACML 2020, 2020.' conference: end_date: 2020-11-20 location: Bangkok, Thailand name: 12th Asian Conference on Machine Learning start_date: 2020-11-18 date_created: 2020-08-25T12:09:28Z date_updated: 2022-01-06T06:53:28Z department: - _id: '34' - _id: '355' - _id: '26' language: - iso: eng main_file_link: - url: https://arxiv.org/pdf/2007.02816.pdf 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: ACML 2020 status: public title: 'Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis' type: conference user_id: '5786' year: '2020' ... --- _id: '16725' author: - first_name: Cedric full_name: Richter, Cedric id: '50003' last_name: Richter - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier - first_name: Marie-Christine full_name: Jakobs, Marie-Christine last_name: Jakobs - first_name: Heike full_name: Wehrheim, Heike id: '573' last_name: Wehrheim citation: ama: Richter C, Hüllermeier E, Jakobs M-C, Wehrheim H. Algorithm Selection for Software Validation Based on Graph Kernels. Journal of Automated Software Engineering. apa: Richter, C., Hüllermeier, E., Jakobs, M.-C., & Wehrheim, H. (n.d.). Algorithm Selection for Software Validation Based on Graph Kernels. Journal of Automated Software Engineering. bibtex: '@article{Richter_Hüllermeier_Jakobs_Wehrheim, title={Algorithm Selection for Software Validation Based on Graph Kernels}, journal={Journal of Automated Software Engineering}, publisher={Springer}, author={Richter, Cedric and Hüllermeier, Eyke and Jakobs, Marie-Christine and Wehrheim, Heike} }' chicago: Richter, Cedric, Eyke Hüllermeier, Marie-Christine Jakobs, and Heike Wehrheim. “Algorithm Selection for Software Validation Based on Graph Kernels.” Journal of Automated Software Engineering, n.d. ieee: C. Richter, E. Hüllermeier, M.-C. Jakobs, and H. Wehrheim, “Algorithm Selection for Software Validation Based on Graph Kernels,” Journal of Automated Software Engineering. mla: Richter, Cedric, et al. “Algorithm Selection for Software Validation Based on Graph Kernels.” Journal of Automated Software Engineering, Springer. short: C. Richter, E. Hüllermeier, M.-C. Jakobs, H. Wehrheim, Journal of Automated Software Engineering (n.d.). date_created: 2020-04-19T14:08:06Z date_updated: 2022-01-06T06:52:55Z department: - _id: '7' - _id: '77' - _id: '355' language: - iso: eng project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '11' name: SFB 901 - Subproject B3 - _id: '12' name: SFB 901 - Subproject B4 publication: Journal of Automated Software Engineering publication_status: accepted publisher: Springer status: public title: Algorithm Selection for Software Validation Based on Graph Kernels type: journal_article user_id: '477' year: '2020' ... --- _id: '15629' 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. author: - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: 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.' apa: 'Wever, M. D., Tornede, A., Mohr, F., & Hüllermeier, E. (n.d.). LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification. Symposium on Intelligent Data Analysis, Konstanz, Germany.' 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} }' 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.' 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.' mla: 'Wever, Marcel Dominik, et al. LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification. Springer.' short: 'M.D. Wever, A. Tornede, F. Mohr, E. Hüllermeier, in: Springer, n.d.' conference: end_date: 2020-04-27 location: Konstanz, Germany name: Symposium on Intelligent Data Analysis start_date: 2020-04-24 date_created: 2020-01-23T08:44:08Z date_updated: 2022-01-06T06:52:30Z 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_status: accepted publisher: Springer status: public title: 'LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification' type: conference user_id: '5786' year: '2020' ... --- _id: '15025' 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. author: - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Lorijn full_name: van Rooijen, Lorijn id: '58843' last_name: van Rooijen - first_name: Heiko full_name: Hamann, Heiko last_name: Hamann citation: ama: Wever MD, van Rooijen L, Hamann H. Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets. Evolutionary Computation. 2020;28(2):165–193. doi:10.1162/evco_a_00266 apa: Wever, M. D., van Rooijen, L., & Hamann, H. (2020). Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets. Evolutionary Computation, 28(2), 165–193. https://doi.org/10.1162/evco_a_00266 bibtex: '@article{Wever_van Rooijen_Hamann_2020, title={Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets}, volume={28}, DOI={10.1162/evco_a_00266}, number={2}, journal={Evolutionary Computation}, publisher={MIT Press Journals}, author={Wever, Marcel Dominik and van Rooijen, Lorijn and Hamann, Heiko}, year={2020}, pages={165–193} }' chicago: 'Wever, Marcel Dominik, Lorijn van Rooijen, and Heiko Hamann. “Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets.” Evolutionary Computation 28, no. 2 (2020): 165–193. https://doi.org/10.1162/evco_a_00266.' ieee: 'M. D. Wever, L. van Rooijen, and H. Hamann, “Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets,” Evolutionary Computation, vol. 28, no. 2, pp. 165–193, 2020, doi: 10.1162/evco_a_00266.' mla: Wever, Marcel Dominik, et al. “Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets.” Evolutionary Computation, vol. 28, no. 2, MIT Press Journals, 2020, pp. 165–193, doi:10.1162/evco_a_00266. short: M.D. Wever, L. van Rooijen, H. Hamann, Evolutionary Computation 28 (2020) 165–193. date_created: 2019-11-18T14:19:19Z date_updated: 2022-01-06T06:52:15Z department: - _id: '34' - _id: '355' - _id: '26' - _id: '63' - _id: '238' doi: 10.1162/evco_a_00266 intvolume: ' 28' issue: '2' language: - iso: eng page: 165–193 project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '9' name: SFB 901 - Subproject B1 - _id: '10' name: SFB 901 - Subproject B2 - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: Evolutionary Computation publication_status: published publisher: MIT Press Journals related_material: link: - relation: confirmation url: https://www.mitpressjournals.org/doi/pdf/10.1162/evco_a_00266 status: public title: Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets type: journal_article user_id: '15415' volume: 28 year: '2020' ... --- _id: '19523' abstract: - lang: eng 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." author: - first_name: Karlson full_name: Pfannschmidt, Karlson last_name: Pfannschmidt - first_name: Pritha full_name: Gupta, Pritha last_name: Gupta - first_name: Eyke full_name: Hüllermeier, Eyke last_name: Hüllermeier citation: ama: 'Pfannschmidt K, Gupta P, Hüllermeier E. Learning Choice Functions: Concepts and Architectures. arXiv:190110860. 2019.' apa: 'Pfannschmidt, K., Gupta, P., & Hüllermeier, E. (2019). Learning Choice Functions: Concepts and Architectures. ArXiv:1901.10860.' 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} }' chicago: 'Pfannschmidt, Karlson, Pritha Gupta, and Eyke Hüllermeier. “Learning Choice Functions: Concepts and Architectures.” ArXiv:1901.10860, 2019.' ieee: 'K. Pfannschmidt, P. Gupta, and E. Hüllermeier, “Learning Choice Functions: Concepts and Architectures,” arXiv:1901.10860. 2019.' mla: 'Pfannschmidt, Karlson, et al. “Learning Choice Functions: Concepts and Architectures.” ArXiv:1901.10860, 2019.' short: K. Pfannschmidt, P. Gupta, E. Hüllermeier, ArXiv:1901.10860 (2019). date_created: 2020-09-17T10:53:38Z date_updated: 2022-01-06T06:54:06Z department: - _id: '7' - _id: '355' language: - iso: eng project: - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: arXiv:1901.10860 status: public title: 'Learning Choice Functions: Concepts and Architectures' type: preprint user_id: '13472' year: '2019' ... --- _id: '17565' author: - first_name: Marie-Luis full_name: Merten, Marie-Luis last_name: Merten - first_name: Nina full_name: Seemann, Nina last_name: Seemann - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' citation: ama: Merten M-L, Seemann N, Wever MD. Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff. Niederdeutsches Jahrbuch. 2019;(142):124-146. apa: Merten, M.-L., Seemann, N., & Wever, M. D. (2019). Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff. Niederdeutsches Jahrbuch, 142, 124–146. bibtex: '@article{Merten_Seemann_Wever_2019, title={Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff}, number={142}, journal={Niederdeutsches Jahrbuch}, author={Merten, Marie-Luis and Seemann, Nina and Wever, Marcel Dominik}, year={2019}, pages={124–146} }' chicago: 'Merten, Marie-Luis, Nina Seemann, and Marcel Dominik Wever. “Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff.” Niederdeutsches Jahrbuch, no. 142 (2019): 124–46.' ieee: M.-L. Merten, N. Seemann, and M. D. Wever, “Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff,” Niederdeutsches Jahrbuch, no. 142, pp. 124–146, 2019. mla: Merten, Marie-Luis, et al. “Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff.” Niederdeutsches Jahrbuch, no. 142, 2019, pp. 124–46. short: M.-L. Merten, N. Seemann, M.D. Wever, Niederdeutsches Jahrbuch (2019) 124–146. date_created: 2020-08-03T13:55:04Z date_updated: 2022-01-06T06:53:15Z department: - _id: '34' - _id: '355' - _id: '26' issue: '142' language: - iso: ger page: 124-146 project: - _id: '39' name: InterGramm publication: Niederdeutsches Jahrbuch publication_status: published status: public title: Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff type: journal_article user_id: '5786' year: '2019' ... --- _id: '18018' abstract: - lang: eng text: |- A common statistical task lies in showing asymptotic normality of certain statistics. In many of these situations, classical textbook results on weak convergence theory suffice for the problem at hand. However, there are quite some scenarios where stronger results are needed in order to establish an asymptotic normal approximation uniformly over a family of probability measures. In this note we collect some results in this direction. We restrict ourselves to weak convergence in $\mathbb R^d$ with continuous limit measures. author: - first_name: Viktor full_name: Bengs, Viktor last_name: Bengs - first_name: Hajo full_name: Holzmann, Hajo last_name: Holzmann citation: ama: Bengs V, Holzmann H. Uniform approximation in classical weak convergence theory. arXiv:190309864. 2019. apa: Bengs, V., & Holzmann, H. (2019). Uniform approximation in classical weak convergence theory. ArXiv:1903.09864. bibtex: '@article{Bengs_Holzmann_2019, title={Uniform approximation in classical weak convergence theory}, journal={arXiv:1903.09864}, author={Bengs, Viktor and Holzmann, Hajo}, year={2019} }' chicago: Bengs, Viktor, and Hajo Holzmann. “Uniform Approximation in Classical Weak Convergence Theory.” ArXiv:1903.09864, 2019. ieee: V. Bengs and H. Holzmann, “Uniform approximation in classical weak convergence theory,” arXiv:1903.09864. 2019. mla: Bengs, Viktor, and Hajo Holzmann. “Uniform Approximation in Classical Weak Convergence Theory.” ArXiv:1903.09864, 2019. short: V. Bengs, H. Holzmann, ArXiv:1903.09864 (2019). date_created: 2020-08-17T12:10:55Z date_updated: 2022-01-06T06:53:25Z department: - _id: '34' - _id: '7' - _id: '355' publication: arXiv:1903.09864 status: public title: Uniform approximation in classical weak convergence theory type: preprint user_id: '76599' year: '2019' ... --- _id: '8868' author: - 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 - first_name: Alexander full_name: Hetzer, Alexander id: '38209' last_name: Hetzer citation: ama: 'Wever MD, Mohr F, Hüllermeier E, Hetzer A. Towards Automated Machine Learning for Multi-Label Classification. In: ; 2019.' apa: Wever, M. D., Mohr, F., Hüllermeier, E., & Hetzer, A. (2019). Towards Automated Machine Learning for Multi-Label Classification. Presented at the European Conference on Data Analytics (ECDA), Bayreuth, Germany. bibtex: '@inproceedings{Wever_Mohr_Hüllermeier_Hetzer_2019, title={Towards Automated Machine Learning for Multi-Label Classification}, author={Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke and Hetzer, Alexander}, year={2019} }' chicago: Wever, Marcel Dominik, Felix Mohr, Eyke Hüllermeier, and Alexander Hetzer. “Towards Automated Machine Learning for Multi-Label Classification,” 2019. ieee: M. D. Wever, F. Mohr, E. Hüllermeier, and A. Hetzer, “Towards Automated Machine Learning for Multi-Label Classification,” presented at the European Conference on Data Analytics (ECDA), Bayreuth, Germany, 2019. mla: Wever, Marcel Dominik, et al. Towards Automated Machine Learning for Multi-Label Classification. 2019. short: 'M.D. Wever, F. Mohr, E. Hüllermeier, A. Hetzer, in: 2019.' conference: end_date: 2019-03-20 location: Bayreuth, Germany name: European Conference on Data Analytics (ECDA) start_date: 2019-03-18 date_created: 2019-04-10T07:17:55Z date_updated: 2022-01-06T07:04:04Z ddc: - '000' department: - _id: '355' file: - access_level: closed content_type: application/pdf creator: wever date_created: 2019-04-10T07:17:17Z date_updated: 2019-04-10T07:17:17Z file_id: '8870' file_name: Towards_Automated_Machine_Learning_for_Multi_Label_Classification.pdf file_size: '74484' relation: main_file success: 1 file_date_updated: 2019-04-10T07:17:17Z has_accepted_license: '1' 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 status: public title: Towards Automated Machine Learning for Multi-Label Classification type: conference_abstract user_id: '49109' year: '2019' ... --- _id: '10578' author: - first_name: V. K. full_name: Tagne, V. K. last_name: Tagne - first_name: S. full_name: Fotso, S. last_name: Fotso - first_name: 'L. A. ' full_name: 'Fono, L. A. ' last_name: Fono - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: Tagne VK, Fotso S, Fono LA, Hüllermeier E. Choice Functions Generated by Mallows and Plackett–Luce Relations. New Mathematics and Natural Computation. 2019;15(2):191-213. apa: Tagne, V. K., Fotso, S., Fono, L. A., & Hüllermeier, E. (2019). Choice Functions Generated by Mallows and Plackett–Luce Relations. New Mathematics and Natural Computation, 15(2), 191–213. bibtex: '@article{Tagne_Fotso_Fono_Hüllermeier_2019, title={Choice Functions Generated by Mallows and Plackett–Luce Relations}, volume={15}, number={2}, journal={New Mathematics and Natural Computation}, author={Tagne, V. K. and Fotso, S. and Fono, L. A. and Hüllermeier, Eyke}, year={2019}, pages={191–213} }' chicago: 'Tagne, V. K., S. Fotso, L. A. Fono, and Eyke Hüllermeier. “Choice Functions Generated by Mallows and Plackett–Luce Relations.” New Mathematics and Natural Computation 15, no. 2 (2019): 191–213.' ieee: V. K. Tagne, S. Fotso, L. A. Fono, and E. Hüllermeier, “Choice Functions Generated by Mallows and Plackett–Luce Relations,” New Mathematics and Natural Computation, vol. 15, no. 2, pp. 191–213, 2019. mla: Tagne, V. K., et al. “Choice Functions Generated by Mallows and Plackett–Luce Relations.” New Mathematics and Natural Computation, vol. 15, no. 2, 2019, pp. 191–213. short: V.K. Tagne, S. Fotso, L.A. Fono, E. Hüllermeier, New Mathematics and Natural Computation 15 (2019) 191–213. date_created: 2019-07-08T15:34:03Z date_updated: 2022-01-06T06:50:45Z department: - _id: '34' - _id: '355' - _id: '7' intvolume: ' 15' issue: '2' language: - iso: eng page: 191-213 publication: New Mathematics and Natural Computation status: public title: Choice Functions Generated by Mallows and Plackett–Luce Relations type: journal_article user_id: '315' volume: 15 year: '2019' ... --- _id: '15001' author: - first_name: Ines full_name: Couso, Ines last_name: Couso - first_name: Christian full_name: Borgelt, Christian last_name: Borgelt - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier - first_name: Rudolf full_name: Kruse, Rudolf last_name: Kruse citation: ama: 'Couso I, Borgelt C, Hüllermeier E, Kruse R. Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning. IEEE Computational Intelligence Magazine. 2019:31-44. doi:10.1109/mci.2018.2881642' apa: 'Couso, I., Borgelt, C., Hüllermeier, E., & Kruse, R. (2019). Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning. IEEE Computational Intelligence Magazine, 31–44. https://doi.org/10.1109/mci.2018.2881642' bibtex: '@article{Couso_Borgelt_Hüllermeier_Kruse_2019, title={Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning}, DOI={10.1109/mci.2018.2881642}, journal={IEEE Computational Intelligence Magazine}, author={Couso, Ines and Borgelt, Christian and Hüllermeier, Eyke and Kruse, Rudolf}, year={2019}, pages={31–44} }' chicago: 'Couso, Ines, Christian Borgelt, Eyke Hüllermeier, and Rudolf Kruse. “Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning.” IEEE Computational Intelligence Magazine, 2019, 31–44. https://doi.org/10.1109/mci.2018.2881642.' ieee: 'I. Couso, C. Borgelt, E. Hüllermeier, and R. Kruse, “Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning,” IEEE Computational Intelligence Magazine, pp. 31–44, 2019.' mla: 'Couso, Ines, et al. “Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning.” IEEE Computational Intelligence Magazine, 2019, pp. 31–44, doi:10.1109/mci.2018.2881642.' short: I. Couso, C. Borgelt, E. Hüllermeier, R. Kruse, IEEE Computational Intelligence Magazine (2019) 31–44. date_created: 2019-11-15T10:11:37Z date_updated: 2022-01-06T06:52:13Z department: - _id: '34' - _id: '355' doi: 10.1109/mci.2018.2881642 language: - iso: eng page: 31-44 publication: IEEE Computational Intelligence Magazine publication_identifier: issn: - 1556-603X - 1556-6048 publication_status: published status: public title: 'Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning' type: journal_article user_id: '315' year: '2019' ... --- _id: '15002' abstract: - lang: eng text: Many problem settings in machine learning are concerned with the simultaneous prediction of multiple target variables of diverse type. Amongst others, such problem settings arise in multivariate regression, multi-label classification, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion. These subfields of machine learning are typically studied in isolation, without highlighting or exploring important relationships. In this paper, we present a unifying view on what we call multi-target prediction (MTP) problems and methods. First, we formally discuss commonalities and differences between existing MTP problems. To this end, we introduce a general framework that covers the above subfields as special cases. As a second contribution, we provide a structured overview of MTP methods. This is accomplished by identifying a number of key properties, which distinguish such methods and determine their suitability for different types of problems. Finally, we also discuss a few challenges for future research. author: - first_name: Willem full_name: Waegeman, Willem last_name: Waegeman - first_name: Krzysztof full_name: Dembczynski, Krzysztof last_name: Dembczynski - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Waegeman W, Dembczynski K, Hüllermeier E. Multi-target prediction: a unifying view on problems and methods. Data Mining and Knowledge Discovery. 2019;33(2):293-324. doi:10.1007/s10618-018-0595-5' apa: 'Waegeman, W., Dembczynski, K., & Hüllermeier, E. (2019). Multi-target prediction: a unifying view on problems and methods. Data Mining and Knowledge Discovery, 33(2), 293–324. https://doi.org/10.1007/s10618-018-0595-5' bibtex: '@article{Waegeman_Dembczynski_Hüllermeier_2019, title={Multi-target prediction: a unifying view on problems and methods}, volume={33}, DOI={10.1007/s10618-018-0595-5}, number={2}, journal={Data Mining and Knowledge Discovery}, author={Waegeman, Willem and Dembczynski, Krzysztof and Hüllermeier, Eyke}, year={2019}, pages={293–324} }' chicago: 'Waegeman, Willem, Krzysztof Dembczynski, and Eyke Hüllermeier. “Multi-Target Prediction: A Unifying View on Problems and Methods.” Data Mining and Knowledge Discovery 33, no. 2 (2019): 293–324. https://doi.org/10.1007/s10618-018-0595-5.' ieee: 'W. Waegeman, K. Dembczynski, and E. Hüllermeier, “Multi-target prediction: a unifying view on problems and methods,” Data Mining and Knowledge Discovery, vol. 33, no. 2, pp. 293–324, 2019.' mla: 'Waegeman, Willem, et al. “Multi-Target Prediction: A Unifying View on Problems and Methods.” Data Mining and Knowledge Discovery, vol. 33, no. 2, 2019, pp. 293–324, doi:10.1007/s10618-018-0595-5.' short: W. Waegeman, K. Dembczynski, E. Hüllermeier, Data Mining and Knowledge Discovery 33 (2019) 293–324. date_created: 2019-11-15T10:16:34Z date_updated: 2022-01-06T06:52:14Z ddc: - '000' department: - _id: '34' - _id: '355' doi: 10.1007/s10618-018-0595-5 file: - access_level: open_access content_type: application/pdf creator: lettmann date_created: 2020-02-28T12:43:39Z date_updated: 2020-02-28T12:45:26Z file_id: '16155' file_name: multi-target-prediction.pdf file_size: 837808 relation: main_file file_date_updated: 2020-02-28T12:45:26Z has_accepted_license: '1' intvolume: ' 33' issue: '2' language: - iso: eng oa: '1' page: 293-324 publication: Data Mining and Knowledge Discovery publication_identifier: issn: - 1573-756X status: public title: 'Multi-target prediction: a unifying view on problems and methods' type: journal_article user_id: '315' volume: 33 year: '2019' ... --- _id: '15003' author: - first_name: Thomas full_name: Mortier, Thomas last_name: Mortier - first_name: Marek full_name: Wydmuch, Marek last_name: Wydmuch - first_name: Krzysztof full_name: Dembczynski, Krzysztof last_name: Dembczynski - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier - first_name: Willem full_name: Waegeman, Willem last_name: Waegeman citation: ama: 'Mortier T, Wydmuch M, Dembczynski K, Hüllermeier E, Waegeman W. Set-Valued Prediction in Multi-Class Classification. In: Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8, 2019. ; 2019.' apa: Mortier, T., Wydmuch, M., Dembczynski, K., Hüllermeier, E., & Waegeman, W. (2019). Set-Valued Prediction in Multi-Class Classification. In Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8, 2019. bibtex: '@inproceedings{Mortier_Wydmuch_Dembczynski_Hüllermeier_Waegeman_2019, title={Set-Valued Prediction in Multi-Class Classification}, booktitle={Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8, 2019}, author={Mortier, Thomas and Wydmuch, Marek and Dembczynski, Krzysztof and Hüllermeier, Eyke and Waegeman, Willem}, year={2019} }' chicago: Mortier, Thomas, Marek Wydmuch, Krzysztof Dembczynski, Eyke Hüllermeier, and Willem Waegeman. “Set-Valued Prediction in Multi-Class Classification.” In Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8, 2019, 2019. ieee: T. Mortier, M. Wydmuch, K. Dembczynski, E. Hüllermeier, and W. Waegeman, “Set-Valued Prediction in Multi-Class Classification,” in Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8, 2019, 2019. mla: Mortier, Thomas, et al. “Set-Valued Prediction in Multi-Class Classification.” Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8, 2019, 2019. short: 'T. Mortier, M. Wydmuch, K. Dembczynski, E. Hüllermeier, W. Waegeman, in: Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8, 2019, 2019.' date_created: 2019-11-15T10:20:55Z date_updated: 2022-01-06T06:52:14Z department: - _id: '34' - _id: '355' language: - iso: eng publication: Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8, 2019 status: public title: Set-Valued Prediction in Multi-Class Classification type: conference user_id: '315' year: '2019' ... --- _id: '15004' author: - first_name: Mohsen full_name: Ahmadi Fahandar, Mohsen id: '59547' last_name: Ahmadi Fahandar - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Ahmadi Fahandar M, Hüllermeier E. Feature Selection for Analogy-Based Learning to Rank. In: Discovery Science. Cham; 2019. doi:10.1007/978-3-030-33778-0_22' apa: Ahmadi Fahandar, M., & Hüllermeier, E. (2019). Feature Selection for Analogy-Based Learning to Rank. In Discovery Science. Cham. https://doi.org/10.1007/978-3-030-33778-0_22 bibtex: '@inbook{Ahmadi Fahandar_Hüllermeier_2019, place={Cham}, title={Feature Selection for Analogy-Based Learning to Rank}, DOI={10.1007/978-3-030-33778-0_22}, booktitle={Discovery Science}, author={Ahmadi Fahandar, Mohsen and Hüllermeier, Eyke}, year={2019} }' chicago: Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Feature Selection for Analogy-Based Learning to Rank.” In Discovery Science. Cham, 2019. https://doi.org/10.1007/978-3-030-33778-0_22. ieee: M. Ahmadi Fahandar and E. Hüllermeier, “Feature Selection for Analogy-Based Learning to Rank,” in Discovery Science, Cham, 2019. mla: Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Feature Selection for Analogy-Based Learning to Rank.” Discovery Science, 2019, doi:10.1007/978-3-030-33778-0_22. short: 'M. Ahmadi Fahandar, E. Hüllermeier, in: Discovery Science, Cham, 2019.' date_created: 2019-11-15T10:24:45Z date_updated: 2022-01-06T06:52:14Z department: - _id: '34' - _id: '355' doi: 10.1007/978-3-030-33778-0_22 language: - iso: eng place: Cham publication: Discovery Science publication_identifier: isbn: - '9783030337773' - '9783030337780' issn: - 0302-9743 - 1611-3349 publication_status: published status: public title: Feature Selection for Analogy-Based Learning to Rank type: book_chapter user_id: '315' year: '2019' ... --- _id: '15005' author: - first_name: Mohsen full_name: Ahmadi Fahandar, Mohsen id: '59547' last_name: Ahmadi Fahandar - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Ahmadi Fahandar M, Hüllermeier E. Analogy-Based Preference Learning with Kernels. In: KI 2019: Advances in Artificial Intelligence. Cham; 2019. doi:10.1007/978-3-030-30179-8_3' apa: 'Ahmadi Fahandar, M., & Hüllermeier, E. (2019). Analogy-Based Preference Learning with Kernels. In KI 2019: Advances in Artificial Intelligence. Cham. https://doi.org/10.1007/978-3-030-30179-8_3' bibtex: '@inbook{Ahmadi Fahandar_Hüllermeier_2019, place={Cham}, title={Analogy-Based Preference Learning with Kernels}, DOI={10.1007/978-3-030-30179-8_3}, booktitle={KI 2019: Advances in Artificial Intelligence}, author={Ahmadi Fahandar, Mohsen and Hüllermeier, Eyke}, year={2019} }' chicago: 'Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Analogy-Based Preference Learning with Kernels.” In KI 2019: Advances in Artificial Intelligence. Cham, 2019. https://doi.org/10.1007/978-3-030-30179-8_3.' ieee: 'M. Ahmadi Fahandar and E. Hüllermeier, “Analogy-Based Preference Learning with Kernels,” in KI 2019: Advances in Artificial Intelligence, Cham, 2019.' mla: 'Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Analogy-Based Preference Learning with Kernels.” KI 2019: Advances in Artificial Intelligence, 2019, doi:10.1007/978-3-030-30179-8_3.' short: 'M. Ahmadi Fahandar, E. Hüllermeier, in: KI 2019: Advances in Artificial Intelligence, Cham, 2019.' date_created: 2019-11-15T10:30:10Z date_updated: 2022-01-06T06:52:14Z department: - _id: '34' - _id: '355' doi: 10.1007/978-3-030-30179-8_3 language: - iso: eng place: Cham publication: 'KI 2019: Advances in Artificial Intelligence' publication_identifier: isbn: - '9783030301781' - '9783030301798' issn: - 0302-9743 - 1611-3349 publication_status: published status: public title: Analogy-Based Preference Learning with Kernels type: book_chapter user_id: '315' year: '2019' ... --- _id: '15006' author: - first_name: Vu-Linh full_name: Nguyen, Vu-Linh last_name: Nguyen - first_name: Sébastien full_name: Destercke, Sébastien last_name: Destercke - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Nguyen V-L, Destercke S, Hüllermeier E. Epistemic Uncertainty Sampling. In: Discovery Science. Cham; 2019. doi:10.1007/978-3-030-33778-0_7' apa: Nguyen, V.-L., Destercke, S., & Hüllermeier, E. (2019). Epistemic Uncertainty Sampling. In Discovery Science. Cham. https://doi.org/10.1007/978-3-030-33778-0_7 bibtex: '@inbook{Nguyen_Destercke_Hüllermeier_2019, place={Cham}, title={Epistemic Uncertainty Sampling}, DOI={10.1007/978-3-030-33778-0_7}, booktitle={Discovery Science}, author={Nguyen, Vu-Linh and Destercke, Sébastien and Hüllermeier, Eyke}, year={2019} }' chicago: Nguyen, Vu-Linh, Sébastien Destercke, and Eyke Hüllermeier. “Epistemic Uncertainty Sampling.” In Discovery Science. Cham, 2019. https://doi.org/10.1007/978-3-030-33778-0_7. ieee: V.-L. Nguyen, S. Destercke, and E. Hüllermeier, “Epistemic Uncertainty Sampling,” in Discovery Science, Cham, 2019. mla: Nguyen, Vu-Linh, et al. “Epistemic Uncertainty Sampling.” Discovery Science, 2019, doi:10.1007/978-3-030-33778-0_7. short: 'V.-L. Nguyen, S. Destercke, E. Hüllermeier, in: Discovery Science, Cham, 2019.' date_created: 2019-11-15T10:35:08Z date_updated: 2022-01-06T06:52:14Z department: - _id: '34' - _id: '355' doi: 10.1007/978-3-030-33778-0_7 language: - iso: eng place: Cham publication: Discovery Science publication_identifier: isbn: - '9783030337773' - '9783030337780' issn: - 0302-9743 - 1611-3349 publication_status: published status: public title: Epistemic Uncertainty Sampling type: book_chapter user_id: '49109' year: '2019' ... --- _id: '15007' author: - 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: 'Melnikov V, Hüllermeier E. Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA. In: Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101). ; 2019. doi:10.1016/j.jmva.2019.02.017' apa: 'Melnikov, V., & Hüllermeier, E. (2019). Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA. In Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101). https://doi.org/10.1016/j.jmva.2019.02.017' bibtex: '@inproceedings{Melnikov_Hüllermeier_2019, title={Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA}, DOI={10.1016/j.jmva.2019.02.017}, booktitle={Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101)}, author={Melnikov, Vitaly and Hüllermeier, Eyke}, year={2019} }' chicago: 'Melnikov, Vitaly, and Eyke Hüllermeier. “Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA.” In Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101), 2019. https://doi.org/10.1016/j.jmva.2019.02.017.' ieee: 'V. Melnikov and E. Hüllermeier, “Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA,” in Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101), 2019.' mla: 'Melnikov, Vitaly, and Eyke Hüllermeier. “Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA.” Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101), 2019, doi:10.1016/j.jmva.2019.02.017.' short: 'V. Melnikov, E. Hüllermeier, in: Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101), 2019.' date_created: 2019-11-15T10:43:26Z date_updated: 2022-01-06T06:52:14Z ddc: - '000' department: - _id: '34' - _id: '355' - _id: '7' doi: 10.1016/j.jmva.2019.02.017 file: - access_level: open_access content_type: application/pdf creator: lettmann date_created: 2020-02-28T12:47:07Z date_updated: 2020-02-28T12:47:07Z file_id: '16156' file_name: learning-to-aggregate-owa.pdf file_size: 2331320 relation: main_file file_date_updated: 2020-02-28T12:47:07Z has_accepted_license: '1' language: - iso: eng oa: '1' project: - _id: '10' name: SFB 901 - Subproject B2 - _id: '3' name: SFB 901 - Project Area B - _id: '1' name: SFB 901 publication: Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101) publication_status: published status: public title: 'Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA' type: conference user_id: '477' year: '2019' ... --- _id: '15009' author: - first_name: Nico full_name: Epple, Nico last_name: Epple - first_name: Simone full_name: Dari, Simone last_name: Dari - first_name: Ludwig full_name: Drees, Ludwig last_name: Drees - first_name: Valentin full_name: Protschky, Valentin last_name: Protschky - first_name: Andreas full_name: Riener, Andreas last_name: Riener citation: ama: 'Epple N, Dari S, Drees L, Protschky V, Riener A. Influence of Cruise Control on Driver Guidance - a Comparison between System Generations and Countries. In: 2019 IEEE Intelligent Vehicles Symposium (IV). ; 2019. doi:10.1109/ivs.2019.8814100' apa: Epple, N., Dari, S., Drees, L., Protschky, V., & Riener, A. (2019). Influence of Cruise Control on Driver Guidance - a Comparison between System Generations and Countries. In 2019 IEEE Intelligent Vehicles Symposium (IV). https://doi.org/10.1109/ivs.2019.8814100 bibtex: '@inproceedings{Epple_Dari_Drees_Protschky_Riener_2019, title={Influence of Cruise Control on Driver Guidance - a Comparison between System Generations and Countries}, DOI={10.1109/ivs.2019.8814100}, booktitle={2019 IEEE Intelligent Vehicles Symposium (IV)}, author={Epple, Nico and Dari, Simone and Drees, Ludwig and Protschky, Valentin and Riener, Andreas}, year={2019} }' chicago: Epple, Nico, Simone Dari, Ludwig Drees, Valentin Protschky, and Andreas Riener. “Influence of Cruise Control on Driver Guidance - a Comparison between System Generations and Countries.” In 2019 IEEE Intelligent Vehicles Symposium (IV), 2019. https://doi.org/10.1109/ivs.2019.8814100. ieee: N. Epple, S. Dari, L. Drees, V. Protschky, and A. Riener, “Influence of Cruise Control on Driver Guidance - a Comparison between System Generations and Countries,” in 2019 IEEE Intelligent Vehicles Symposium (IV), 2019. mla: Epple, Nico, et al. “Influence of Cruise Control on Driver Guidance - a Comparison between System Generations and Countries.” 2019 IEEE Intelligent Vehicles Symposium (IV), 2019, doi:10.1109/ivs.2019.8814100. short: 'N. Epple, S. Dari, L. Drees, V. Protschky, A. Riener, in: 2019 IEEE Intelligent Vehicles Symposium (IV), 2019.' date_created: 2019-11-15T10:54:04Z date_updated: 2022-01-06T06:52:14Z department: - _id: '34' - _id: '355' doi: 10.1109/ivs.2019.8814100 language: - iso: eng publication: 2019 IEEE Intelligent Vehicles Symposium (IV) publication_identifier: isbn: - '9781728105604' publication_status: published status: public title: Influence of Cruise Control on Driver Guidance - a Comparison between System Generations and Countries type: conference user_id: '315' year: '2019' ... --- _id: '15011' 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. Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking. In: Hoffmann F, Hüllermeier E, Mikut R, eds. Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019. KIT Scientific Publishing, Karlsruhe; 2019:135-146.' apa: 'Tornede, A., Wever, M. D., & Hüllermeier, E. (2019). Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking. In F. Hoffmann, E. Hüllermeier, & R. Mikut (Eds.), Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019 (pp. 135–146). Dortmund: KIT Scientific Publishing, Karlsruhe.' bibtex: '@inproceedings{Tornede_Wever_Hüllermeier_2019, title={Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking}, booktitle={Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019}, publisher={KIT Scientific Publishing, Karlsruhe}, author={Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}, editor={Hoffmann, Frank and Hüllermeier, Eyke and Mikut, RalfEditors}, year={2019}, pages={135–146} }' chicago: 'Tornede, Alexander, Marcel Dominik Wever, and Eyke Hüllermeier. “Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking.” In Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019, edited by Frank Hoffmann, Eyke Hüllermeier, and Ralf Mikut, 135–46. KIT Scientific Publishing, Karlsruhe, 2019.' ieee: 'A. Tornede, M. D. Wever, and E. Hüllermeier, “Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking,” in Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019, Dortmund, 2019, pp. 135–146.' mla: 'Tornede, Alexander, et al. “Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking.” Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019, edited by Frank Hoffmann et al., KIT Scientific Publishing, Karlsruhe, 2019, pp. 135–46.' short: 'A. Tornede, M.D. Wever, E. Hüllermeier, in: F. Hoffmann, E. Hüllermeier, R. Mikut (Eds.), Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019, KIT Scientific Publishing, Karlsruhe, 2019, pp. 135–146.' conference: end_date: 2019-11-29 location: Dortmund name: 29. Workshop Computational Intelligence start_date: 2019-11-28 date_created: 2019-11-15T13:29:25Z date_updated: 2022-01-06T06:52:14Z ddc: - '006' department: - _id: '355' editor: - first_name: Frank full_name: Hoffmann, Frank last_name: Hoffmann - first_name: Eyke full_name: Hüllermeier, Eyke last_name: Hüllermeier - first_name: Ralf full_name: Mikut, Ralf last_name: Mikut file: - access_level: open_access content_type: application/pdf creator: ahetzer date_created: 2020-05-25T08:01:31Z date_updated: 2020-05-25T08:01:31Z file_id: '17060' file_name: ci_workshop_tornede.pdf file_size: 468825 relation: main_file file_date_updated: 2020-05-25T08:01:31Z has_accepted_license: '1' language: - iso: eng oa: '1' page: 135-146 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: Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019 publication_identifier: isbn: - 978-3-7315-0979-0 publication_status: published publisher: KIT Scientific Publishing, Karlsruhe status: public title: 'Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking' type: conference user_id: '38209' year: '2019' ... --- _id: '15013' author: - first_name: Klaus full_name: Brinker, Klaus last_name: Brinker - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Brinker K, Hüllermeier E. A Reduction of Label Ranking to Multiclass Classification. In: Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases. Würzburg, Germany; 2019.' apa: Brinker, K., & Hüllermeier, E. (2019). A Reduction of Label Ranking to Multiclass Classification. In Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases. Würzburg, Germany. bibtex: '@inproceedings{Brinker_Hüllermeier_2019, place={Würzburg, Germany}, title={A Reduction of Label Ranking to Multiclass Classification}, booktitle={Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases}, author={Brinker, Klaus and Hüllermeier, Eyke}, year={2019} }' chicago: Brinker, Klaus, and Eyke Hüllermeier. “A Reduction of Label Ranking to Multiclass Classification.” In Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases. Würzburg, Germany, 2019. ieee: K. Brinker and E. Hüllermeier, “A Reduction of Label Ranking to Multiclass Classification,” in Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases, 2019. mla: Brinker, Klaus, and Eyke Hüllermeier. “A Reduction of Label Ranking to Multiclass Classification.” Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases, 2019. short: 'K. Brinker, E. Hüllermeier, in: Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases, Würzburg, Germany, 2019.' date_created: 2019-11-18T07:26:43Z date_updated: 2022-01-06T06:52:14Z department: - _id: '34' - _id: '355' - _id: '7' language: - iso: eng place: Würzburg, Germany publication: Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases status: public title: A Reduction of Label Ranking to Multiclass Classification type: conference user_id: '315' year: '2019' ... --- _id: '15014' author: - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier - first_name: Ines full_name: Couso, Ines last_name: Couso - first_name: Sebastian full_name: Diestercke, Sebastian last_name: Diestercke citation: ama: 'Hüllermeier E, Couso I, Diestercke S. Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants. In: Proceedings SUM 2019, International Conference on Scalable Uncertainty Management. ; 2019.' apa: 'Hüllermeier, E., Couso, I., & Diestercke, S. (2019). Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants. In Proceedings SUM 2019, International Conference on Scalable Uncertainty Management.' bibtex: '@inproceedings{Hüllermeier_Couso_Diestercke_2019, title={Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants}, booktitle={Proceedings SUM 2019, International Conference on Scalable Uncertainty Management}, author={Hüllermeier, Eyke and Couso, Ines and Diestercke, Sebastian}, year={2019} }' chicago: 'Hüllermeier, Eyke, Ines Couso, and Sebastian Diestercke. “Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants.” In Proceedings SUM 2019, International Conference on Scalable Uncertainty Management, 2019.' ieee: 'E. Hüllermeier, I. Couso, and S. Diestercke, “Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants,” in Proceedings SUM 2019, International Conference on Scalable Uncertainty Management, 2019.' mla: 'Hüllermeier, Eyke, et al. “Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants.” Proceedings SUM 2019, International Conference on Scalable Uncertainty Management, 2019.' short: 'E. Hüllermeier, I. Couso, S. Diestercke, in: Proceedings SUM 2019, International Conference on Scalable Uncertainty Management, 2019.' date_created: 2019-11-18T07:38:13Z date_updated: 2022-01-06T06:52:14Z department: - _id: '34' - _id: '355' - _id: '7' language: - iso: eng publication: Proceedings SUM 2019, International Conference on Scalable Uncertainty Management status: public title: 'Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants' type: conference user_id: '315' year: '2019' ... --- _id: '15015' author: - first_name: Sascha full_name: Henzgen, Sascha last_name: Henzgen - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: Henzgen S, Hüllermeier E. Mining Rank Data. ACM Transactions on Knowledge Discovery from Data. 2019:1-36. doi:10.1145/3363572 apa: Henzgen, S., & Hüllermeier, E. (2019). Mining Rank Data. ACM Transactions on Knowledge Discovery from Data, 1–36. https://doi.org/10.1145/3363572 bibtex: '@article{Henzgen_Hüllermeier_2019, title={Mining Rank Data}, DOI={10.1145/3363572}, journal={ACM Transactions on Knowledge Discovery from Data}, author={Henzgen, Sascha and Hüllermeier, Eyke}, year={2019}, pages={1–36} }' chicago: Henzgen, Sascha, and Eyke Hüllermeier. “Mining Rank Data.” ACM Transactions on Knowledge Discovery from Data, 2019, 1–36. https://doi.org/10.1145/3363572. ieee: S. Henzgen and E. Hüllermeier, “Mining Rank Data,” ACM Transactions on Knowledge Discovery from Data, pp. 1–36, 2019. mla: Henzgen, Sascha, and Eyke Hüllermeier. “Mining Rank Data.” ACM Transactions on Knowledge Discovery from Data, 2019, pp. 1–36, doi:10.1145/3363572. short: S. Henzgen, E. Hüllermeier, ACM Transactions on Knowledge Discovery from Data (2019) 1–36. date_created: 2019-11-18T07:40:27Z date_updated: 2022-01-06T06:52:14Z department: - _id: '34' - _id: '355' - _id: '7' doi: 10.1145/3363572 language: - iso: eng page: 1-36 publication: ACM Transactions on Knowledge Discovery from Data publication_identifier: issn: - 1556-4681 publication_status: published status: public title: Mining Rank Data type: journal_article user_id: '315' year: '2019' ... --- _id: '14027' author: - first_name: Viktor full_name: Bengs, Viktor id: '76599' last_name: Bengs - first_name: Matthias full_name: Eulert, Matthias last_name: Eulert - first_name: Hajo full_name: Holzmann, Hajo last_name: Holzmann citation: ama: Bengs V, Eulert M, Holzmann H. Asymptotic confidence sets for the jump curve in bivariate regression problems. Journal of Multivariate Analysis. 2019:291-312. doi:10.1016/j.jmva.2019.02.017 apa: Bengs, V., Eulert, M., & Holzmann, H. (2019). Asymptotic confidence sets for the jump curve in bivariate regression problems. Journal of Multivariate Analysis, 291–312. https://doi.org/10.1016/j.jmva.2019.02.017 bibtex: '@article{Bengs_Eulert_Holzmann_2019, title={Asymptotic confidence sets for the jump curve in bivariate regression problems}, DOI={10.1016/j.jmva.2019.02.017}, journal={Journal of Multivariate Analysis}, author={Bengs, Viktor and Eulert, Matthias and Holzmann, Hajo}, year={2019}, pages={291–312} }' chicago: Bengs, Viktor, Matthias Eulert, and Hajo Holzmann. “Asymptotic Confidence Sets for the Jump Curve in Bivariate Regression Problems.” Journal of Multivariate Analysis, 2019, 291–312. https://doi.org/10.1016/j.jmva.2019.02.017. ieee: V. Bengs, M. Eulert, and H. Holzmann, “Asymptotic confidence sets for the jump curve in bivariate regression problems,” Journal of Multivariate Analysis, pp. 291–312, 2019. mla: Bengs, Viktor, et al. “Asymptotic Confidence Sets for the Jump Curve in Bivariate Regression Problems.” Journal of Multivariate Analysis, 2019, pp. 291–312, doi:10.1016/j.jmva.2019.02.017. short: V. Bengs, M. Eulert, H. Holzmann, Journal of Multivariate Analysis (2019) 291–312. date_created: 2019-10-30T14:22:57Z date_updated: 2022-01-06T06:51:52Z department: - _id: '34' - _id: '355' doi: 10.1016/j.jmva.2019.02.017 language: - iso: eng page: 291-312 publication: Journal of Multivariate Analysis publication_identifier: issn: - 0047-259X publication_status: published status: public title: Asymptotic confidence sets for the jump curve in bivariate regression problems type: journal_article user_id: '76599' year: '2019' ... --- _id: '14028' author: - first_name: Viktor full_name: Bengs, Viktor id: '76599' last_name: Bengs - first_name: Hajo full_name: Holzmann, Hajo last_name: Holzmann citation: ama: Bengs V, Holzmann H. Adaptive confidence sets for kink estimation. Electronic Journal of Statistics. 2019:1523-1579. doi:10.1214/19-ejs1555 apa: Bengs, V., & Holzmann, H. (2019). Adaptive confidence sets for kink estimation. Electronic Journal of Statistics, 1523–1579. https://doi.org/10.1214/19-ejs1555 bibtex: '@article{Bengs_Holzmann_2019, title={Adaptive confidence sets for kink estimation}, DOI={10.1214/19-ejs1555}, journal={Electronic Journal of Statistics}, author={Bengs, Viktor and Holzmann, Hajo}, year={2019}, pages={1523–1579} }' chicago: Bengs, Viktor, and Hajo Holzmann. “Adaptive Confidence Sets for Kink Estimation.” Electronic Journal of Statistics, 2019, 1523–79. https://doi.org/10.1214/19-ejs1555. ieee: V. Bengs and H. Holzmann, “Adaptive confidence sets for kink estimation,” Electronic Journal of Statistics, pp. 1523–1579, 2019. mla: Bengs, Viktor, and Hajo Holzmann. “Adaptive Confidence Sets for Kink Estimation.” Electronic Journal of Statistics, 2019, pp. 1523–79, doi:10.1214/19-ejs1555. short: V. Bengs, H. Holzmann, Electronic Journal of Statistics (2019) 1523–1579. date_created: 2019-10-30T14:25:16Z date_updated: 2022-01-06T06:51:52Z department: - _id: '34' - _id: '355' doi: 10.1214/19-ejs1555 language: - iso: eng page: 1523-1579 publication: Electronic Journal of Statistics publication_identifier: issn: - 1935-7524 publication_status: published status: public title: Adaptive confidence sets for kink estimation type: journal_article user_id: '76599' year: '2019' ... --- _id: '13132' author: - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Mohr F, Wever MD, Tornede A, Hüllermeier E. From Automated to On-The-Fly Machine Learning. In: INFORMATIK 2019: 50 Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft. INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft für Informatik. Bonn: Gesellschaft für Informatik e.V.; 2019:273-274.' apa: 'Mohr, F., Wever, M. D., Tornede, A., & Hüllermeier, E. (2019). From Automated to On-The-Fly Machine Learning. In INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft (pp. 273–274). Bonn: Gesellschaft für Informatik e.V.' bibtex: '@inproceedings{Mohr_Wever_Tornede_Hüllermeier_2019, place={Bonn}, series={INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft für Informatik}, title={From Automated to On-The-Fly Machine Learning}, booktitle={INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft}, publisher={Gesellschaft für Informatik e.V.}, author={Mohr, Felix and Wever, Marcel Dominik and Tornede, Alexander and Hüllermeier, Eyke}, year={2019}, pages={273–274}, collection={INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft für Informatik} }' chicago: 'Mohr, Felix, Marcel Dominik Wever, Alexander Tornede, and Eyke Hüllermeier. “From Automated to On-The-Fly Machine Learning.” In INFORMATIK 2019: 50 Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft, 273–74. INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft Für Informatik. Bonn: Gesellschaft für Informatik e.V., 2019.' ieee: 'F. Mohr, M. D. Wever, A. Tornede, and E. Hüllermeier, “From Automated to On-The-Fly Machine Learning,” in INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft, Kassel, 2019, pp. 273–274.' mla: 'Mohr, Felix, et al. “From Automated to On-The-Fly Machine Learning.” INFORMATIK 2019: 50 Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft, Gesellschaft für Informatik e.V., 2019, pp. 273–74.' short: 'F. Mohr, M.D. Wever, A. Tornede, E. Hüllermeier, in: INFORMATIK 2019: 50 Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft, Gesellschaft für Informatik e.V., Bonn, 2019, pp. 273–274.' conference: end_date: 2019-09-26 location: Kassel name: Informatik 2019 start_date: 2019-09-23 date_created: 2019-09-04T08:44:46Z date_updated: 2022-01-06T06:51:28Z department: - _id: '355' language: - iso: eng page: ' 273-274 ' place: Bonn project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 publication: 'INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft' publisher: Gesellschaft für Informatik e.V. series_title: INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft für Informatik status: public title: From Automated to On-The-Fly Machine Learning type: conference_abstract user_id: '38209' year: '2019' ... --- _id: '10232' abstract: - lang: eng text: Existing tools for automated machine learning, such as Auto-WEKA, TPOT, auto-sklearn, and more recently ML-Plan, have shown impressive results for the tasks of single-label classification and regression. Yet, there is only little work on other types of machine learning problems so far. In particular, there is almost no work on automating the engineering of machine learning solutions for multi-label classification (MLC). We show how the scope of ML-Plan, an AutoML-tool for multi-class classification, can be extended towards MLC using MEKA, which is a multi-label extension of the well-known Java library WEKA. The resulting approach recursively refines MEKA's multi-label classifiers, nesting other multi-label classifiers for meta algorithms and single-label classifiers provided by WEKA as base learners. In our evaluation, we find that the proposed approach yields strong results and performs significantly better than a set of baselines we compare with. author: - 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: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Wever MD, Mohr F, Tornede A, Hüllermeier E. Automating Multi-Label Classification Extending ML-Plan. In: ; 2019.' apa: Wever, M. D., Mohr, F., Tornede, A., & Hüllermeier, E. (2019). Automating Multi-Label Classification Extending ML-Plan. Presented at the 6th ICML Workshop on Automated Machine Learning (AutoML 2019), Long Beach, CA, USA. bibtex: '@inproceedings{Wever_Mohr_Tornede_Hüllermeier_2019, title={Automating Multi-Label Classification Extending ML-Plan}, author={Wever, Marcel Dominik and Mohr, Felix and Tornede, Alexander and Hüllermeier, Eyke}, year={2019} }' chicago: Wever, Marcel Dominik, Felix Mohr, Alexander Tornede, and Eyke Hüllermeier. “Automating Multi-Label Classification Extending ML-Plan,” 2019. ieee: M. D. Wever, F. Mohr, A. Tornede, and E. Hüllermeier, “Automating Multi-Label Classification Extending ML-Plan,” presented at the 6th ICML Workshop on Automated Machine Learning (AutoML 2019), Long Beach, CA, USA, 2019. mla: Wever, Marcel Dominik, et al. Automating Multi-Label Classification Extending ML-Plan. 2019. short: 'M.D. Wever, F. Mohr, A. Tornede, E. Hüllermeier, in: 2019.' conference: end_date: 2019-06-15 location: Long Beach, CA, USA name: 6th ICML Workshop on Automated Machine Learning (AutoML 2019) start_date: 2019-06-09 date_created: 2019-06-11T21:33:06Z date_updated: 2022-01-06T06:50:33Z ddc: - '006' department: - _id: '355' file: - access_level: open_access content_type: application/pdf creator: wever date_created: 2019-09-10T08:19:01Z date_updated: 2019-09-10T08:20:44Z file_id: '13177' file_name: Automating_MultiLabel_Classification_Extending_ML-Plan.pdf file_size: 388191 relation: main_file file_date_updated: 2019-09-10T08:20:44Z 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 - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing status: public title: Automating Multi-Label Classification Extending ML-Plan type: conference user_id: '33176' year: '2019' ... --- _id: '20243' author: - first_name: Katharina full_name: Rohlfing, Katharina id: '50352' last_name: Rohlfing - first_name: Giuseppe full_name: Leonardi, Giuseppe last_name: Leonardi - first_name: Iris full_name: Nomikou, Iris last_name: Nomikou - first_name: Joanna full_name: Rączaszek-Leonardi, Joanna last_name: Rączaszek-Leonardi - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Rohlfing K, Leonardi G, Nomikou I, Rączaszek-Leonardi J, Hüllermeier E. Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches. IEEE Transactions on Cognitive and Developmental Systems. Published online 2019. doi:10.1109/TCDS.2019.2892991' apa: 'Rohlfing, K., Leonardi, G., Nomikou, I., Rączaszek-Leonardi, J., & Hüllermeier, E. (2019). Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches. IEEE Transactions on Cognitive and Developmental Systems. https://doi.org/10.1109/TCDS.2019.2892991' bibtex: '@article{Rohlfing_Leonardi_Nomikou_Rączaszek-Leonardi_Hüllermeier_2019, title={Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches}, DOI={10.1109/TCDS.2019.2892991}, journal={IEEE Transactions on Cognitive and Developmental Systems}, author={Rohlfing, Katharina and Leonardi, Giuseppe and Nomikou, Iris and Rączaszek-Leonardi, Joanna and Hüllermeier, Eyke}, year={2019} }' chicago: 'Rohlfing, Katharina, Giuseppe Leonardi, Iris Nomikou, Joanna Rączaszek-Leonardi, and Eyke Hüllermeier. “Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches.” IEEE Transactions on Cognitive and Developmental Systems, 2019. https://doi.org/10.1109/TCDS.2019.2892991.' ieee: 'K. Rohlfing, G. Leonardi, I. Nomikou, J. Rączaszek-Leonardi, and E. Hüllermeier, “Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches,” IEEE Transactions on Cognitive and Developmental Systems, 2019, doi: 10.1109/TCDS.2019.2892991.' mla: 'Rohlfing, Katharina, et al. “Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches.” IEEE Transactions on Cognitive and Developmental Systems, 2019, doi:10.1109/TCDS.2019.2892991.' short: K. Rohlfing, G. Leonardi, I. Nomikou, J. Rączaszek-Leonardi, E. Hüllermeier, IEEE Transactions on Cognitive and Developmental Systems (2019). date_created: 2020-11-02T13:25:49Z date_updated: 2023-02-01T12:39:19Z department: - _id: '749' - _id: '355' doi: 10.1109/TCDS.2019.2892991 language: - iso: eng publication: IEEE Transactions on Cognitive and Developmental Systems status: public title: 'Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches' type: journal_article user_id: '14931' year: '2019' ...