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M., Hüllermeier, E., Mohr, F., Ngonga Ngomo, A.-C., Sherif, M., Tornede, A., & Wever, M. D. (2023). Configuration and Evaluation. In C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, & H. Wehrheim (Eds.), On-The-Fly Computing -- Individualized IT-services in dynamic markets (Vol. 412, pp. 85–104). Heinz Nixdorf Institut, Universität Paderborn. https://doi.org/10.5281/zenodo.8068466","ama":"Hanselle JM, Hüllermeier E, Mohr F, et al. Configuration and Evaluation. In: Haake C-J, Meyer auf der Heide F, Platzner M, Wachsmuth H, Wehrheim H, eds. On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets. Vol 412. Verlagsschriftenreihe des Heinz Nixdorf Instituts. Heinz Nixdorf Institut, Universität Paderborn; 2023:85-104. doi:10.5281/zenodo.8068466","chicago":"Hanselle, Jonas Manuel, Eyke Hüllermeier, Felix Mohr, Axel-Cyrille Ngonga Ngomo, Mohamed Sherif, Alexander Tornede, and Marcel Dominik Wever. “Configuration and Evaluation.” In On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets, edited by Claus-Jochen Haake, Friedhelm Meyer auf der Heide, Marco Platzner, Henning Wachsmuth, and Heike Wehrheim, 412:85–104. Verlagsschriftenreihe Des Heinz Nixdorf Instituts. Paderborn: Heinz Nixdorf Institut, Universität Paderborn, 2023. https://doi.org/10.5281/zenodo.8068466.","mla":"Hanselle, Jonas Manuel, et al. “Configuration and Evaluation.” On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets, edited by Claus-Jochen Haake et al., vol. 412, Heinz Nixdorf Institut, Universität Paderborn, 2023, pp. 85–104, doi:10.5281/zenodo.8068466.","bibtex":"@inbook{Hanselle_Hüllermeier_Mohr_Ngonga Ngomo_Sherif_Tornede_Wever_2023, place={Paderborn}, series={Verlagsschriftenreihe des Heinz Nixdorf Instituts}, title={Configuration and Evaluation}, volume={412}, DOI={10.5281/zenodo.8068466}, booktitle={On-The-Fly Computing -- Individualized IT-services in dynamic markets}, publisher={Heinz Nixdorf Institut, Universität Paderborn}, author={Hanselle, Jonas Manuel and Hüllermeier, Eyke and Mohr, Felix and Ngonga Ngomo, Axel-Cyrille and Sherif, Mohamed and Tornede, Alexander and Wever, Marcel Dominik}, editor={Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}, year={2023}, pages={85–104}, collection={Verlagsschriftenreihe des Heinz Nixdorf Instituts} }","short":"J.M. Hanselle, E. Hüllermeier, F. Mohr, A.-C. Ngonga Ngomo, M. Sherif, A. Tornede, M.D. Wever, in: C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, H. Wehrheim (Eds.), On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets, Heinz Nixdorf Institut, Universität Paderborn, Paderborn, 2023, pp. 85–104.","ieee":"J. M. Hanselle et al., “Configuration and Evaluation,” in On-The-Fly Computing -- Individualized IT-services in dynamic markets, vol. 412, C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, and H. Wehrheim, Eds. Paderborn: Heinz Nixdorf Institut, Universität Paderborn, 2023, pp. 85–104."},"year":"2023","type":"book_chapter","page":"85-104","_id":"45884","intvolume":" 412"},{"department":[{"_id":"355"}],"project":[{"name":"SFB 901 - B2: Konfiguration und Bewertung (B02)","grant_number":"160364472","_id":"10"},{"_id":"3","name":"SFB 901 - B: SFB 901 - Project Area B"},{"name":"SFB 901: SFB 901: On-The-Fly Computing - Individualisierte IT-Dienstleistungen in dynamischen Märkten ","grant_number":"160364472","_id":"1"}],"title":"Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions","language":[{"iso":"eng"}],"date_updated":"2023-08-04T06:01:49Z","oa":"1","doi":"10.17619/UNIPB/1-1780 ","file":[{"file_name":"dissertation_alexander_tornede_final_publishing_compressed.pdf","date_created":"2023-07-24T08:40:35Z","access_level":"open_access","file_size":4300633,"title":" Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions","creator":"ahetzer","file_id":"46118","content_type":"application/pdf","date_updated":"2023-07-24T08:42:01Z","relation":"main_file"}],"author":[{"full_name":"Tornede, Alexander","first_name":"Alexander","id":"38209","last_name":"Tornede"}],"file_date_updated":"2023-07-24T08:42:01Z","status":"public","has_accepted_license":"1","date_created":"2023-06-27T05:20:14Z","user_id":"15504","ddc":["006"],"supervisor":[{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","last_name":"Hüllermeier"}],"type":"dissertation","year":"2023","citation":{"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} }","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 .","ieee":"A. Tornede, Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions. 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 .","ama":"Tornede A. Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions.; 2023. doi:10.17619/UNIPB/1-1780 ","short":"A. Tornede, Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions, 2023.","apa":"Tornede, A. (2023). Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions. https://doi.org/10.17619/UNIPB/1-1780 "},"_id":"45780"},{"series_title":"Verlagsschriftenreihe des Heinz Nixdorf Instituts","language":[{"iso":"eng"}],"date_updated":"2023-08-29T06:44:36Z","oa":"1","doi":"10.17619/UNIPB/1-1797","department":[{"_id":"7"}],"project":[{"grant_number":"160364472","name":"SFB 901: SFB 901: On-The-Fly Computing - Individualisierte IT-Dienstleistungen in dynamischen Märkten ","_id":"1"},{"_id":"2","name":"SFB 901 - A: SFB 901 - Project Area A"},{"name":"SFB 901 - B: SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - C: SFB 901 - Project Area C","_id":"4"},{"_id":"82","name":"SFB 901 - T: SFB 901 - Project Area T"},{"name":"SFB 901 - A1: SFB 901 - Möglichkeiten und Grenzen lokaler Strategien in dynamischen Netzen (Subproject A1)","grant_number":"160364472","_id":"5"},{"name":"SFB 901 - A3: SFB 901 - Der Markt für Services: Anreize, Algorithmen, Implementation (Subproject A3)","grant_number":"160364472","_id":"7"},{"_id":"8","grant_number":"160364472","name":"SFB 901 - A4: SFB 901 - Empirische Analysen in Märkten für OTF Dienstleistungen (Subproject A4)"},{"name":"SFB 901 - B1: SFB 901 - Parametrisierte Servicespezifikation (Subproject B1)","grant_number":"160364472","_id":"9"},{"_id":"10","grant_number":"160364472","name":"SFB 901 - B2: Konfiguration und Bewertung (B02)"},{"name":"SFB 901 - B3: SFB 901 - Subproject B3","_id":"11"},{"_id":"12","name":"SFB 901 - B4: SFB 901 - Subproject B4"},{"name":"SFB 901 - C1: SFB 901 - Subproject C1","_id":"13"},{"name":"SFB 901 - C2: SFB 901 - On-The-Fly Compute Centers I: Heterogene Ausführungsumgebungen (Subproject C2)","grant_number":"160364472","_id":"14"},{"name":"SFB 901 - C4: SFB 901 - On-The-Fly Compute Centers II: Ausführung komponierter Dienste in konfigurierbaren Rechenzentren (Subproject C4)","grant_number":"160364472","_id":"16"},{"name":"SFB 901 - C5: SFB 901 - Subproject C5","_id":"17"},{"name":"SFB 901 - T1: SFB 901 -Subproject T1","_id":"83"},{"name":"SFB 901 - T2: SFB 901 -Subproject T2","_id":"84"}],"publication_identifier":{"unknown":["978-3-947647-31-6"]},"place":"Paderborn","title":"On-The-Fly Computing -- Individualized IT-services in dynamic markets","citation":{"short":"C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, H. Wehrheim, On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets, Heinz Nixdorf Institut, Universität Paderborn, Paderborn, 2023.","ieee":"C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, and H. Wehrheim, On-The-Fly Computing -- Individualized IT-services in dynamic markets, vol. 412. Paderborn: Heinz Nixdorf Institut, Universität Paderborn, 2023.","ama":"Haake C-J, Meyer auf der Heide F, Platzner M, Wachsmuth H, Wehrheim H. On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets. Vol 412. Heinz Nixdorf Institut, Universität Paderborn; 2023. doi:10.17619/UNIPB/1-1797","apa":"Haake, C.-J., Meyer auf der Heide, F., Platzner, M., Wachsmuth, H., & Wehrheim, H. (2023). On-The-Fly Computing -- Individualized IT-services in dynamic markets (Vol. 412). Heinz Nixdorf Institut, Universität Paderborn. https://doi.org/10.17619/UNIPB/1-1797","chicago":"Haake, Claus-Jochen, Friedhelm Meyer auf der Heide, Marco Platzner, Henning Wachsmuth, and Heike Wehrheim. On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets. Vol. 412. Verlagsschriftenreihe Des Heinz Nixdorf Instituts. Paderborn: Heinz Nixdorf Institut, Universität Paderborn, 2023. https://doi.org/10.17619/UNIPB/1-1797.","mla":"Haake, Claus-Jochen, et al. On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets. Heinz Nixdorf Institut, Universität Paderborn, 2023, doi:10.17619/UNIPB/1-1797.","bibtex":"@book{Haake_Meyer auf der Heide_Platzner_Wachsmuth_Wehrheim_2023, place={Paderborn}, series={Verlagsschriftenreihe des Heinz Nixdorf Instituts}, title={On-The-Fly Computing -- Individualized IT-services in dynamic markets}, volume={412}, DOI={10.17619/UNIPB/1-1797}, publisher={Heinz Nixdorf Institut, Universität Paderborn}, author={Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}, year={2023}, collection={Verlagsschriftenreihe des Heinz Nixdorf Instituts} }"},"type":"book","year":"2023","page":"247","_id":"45863","intvolume":" 412","file":[{"file_size":15480050,"creator":"ups","file_id":"45864","content_type":"application/pdf","date_updated":"2023-07-05T07:19:14Z","relation":"main_file","file_name":"SFB-Buch-Final.pdf","date_created":"2023-07-05T07:15:55Z","access_level":"open_access"}],"publisher":"Heinz Nixdorf Institut, Universität Paderborn","author":[{"first_name":"Claus-Jochen","full_name":"Haake, Claus-Jochen","last_name":"Haake","id":"20801"},{"id":"15523","last_name":"Meyer auf der Heide","full_name":"Meyer auf der Heide, Friedhelm","first_name":"Friedhelm"},{"full_name":"Platzner, Marco","first_name":"Marco","id":"398","last_name":"Platzner"},{"last_name":"Wachsmuth","id":"3900","first_name":"Henning","full_name":"Wachsmuth, Henning"},{"first_name":"Heike","full_name":"Wehrheim, Heike","last_name":"Wehrheim","id":"573"}],"file_date_updated":"2023-07-05T07:19:14Z","has_accepted_license":"1","status":"public","date_created":"2023-07-05T07:16:51Z","alternative_title":["Collaborative Research Centre 901 (2011 – 2023)"],"volume":412,"abstract":[{"text":"In the proposal for our CRC in 2011, we formulated a vision of markets for\r\nIT services that describes an approach to the provision of such services\r\nthat was novel at that time and, to a large extent, remains so today:\r\n„Our vision of on-the-fly computing is that of IT services individually and\r\nautomatically configured and brought to execution from flexibly combinable\r\nservices traded on markets. At the same time, we aim at organizing\r\nmarkets whose participants maintain a lively market of services through\r\nappropriate entrepreneurial actions.“\r\nOver the last 12 years, we have developed methods and techniques to\r\naddress problems critical to the convenient, efficient, and secure use of\r\non-the-fly computing. Among other things, we have made the description\r\nof services more convenient by allowing natural language input,\r\nincreased the quality of configured services through (natural language)\r\ninteraction and more efficient configuration processes and analysis\r\nprocedures, made the quality of (the products of) providers in the\r\nmarketplace transparent through reputation systems, and increased the\r\nresource efficiency of execution through reconfigurable heterogeneous\r\ncomputing nodes and an integrated treatment of service description and\r\nconfiguration. We have also developed network infrastructures that have\r\na high degree of adaptivity, scalability, efficiency, and reliability, and\r\nprovide cryptographic guarantees of anonymity and security for market\r\nparticipants and their products and services.\r\nTo demonstrate the pervasiveness of the OTF computing approach, we\r\nhave implemented a proof-of-concept for OTF computing that can run\r\ntypical scenarios of an OTF market. We illustrated the approach using\r\na cutting-edge application scenario – automated machine learning (AutoML).\r\nFinally, we have been pushing our work for the perpetuation of\r\nOn-The-Fly Computing beyond the SFB and sharing the expertise gained\r\nin the SFB in events with industry partners as well as transfer projects.\r\nThis work required a broad spectrum of expertise. Computer scientists\r\nand economists with research interests such as computer networks and\r\ndistributed algorithms, security and cryptography, software engineering\r\nand verification, configuration and machine learning, computer engineering\r\nand HPC, microeconomics and game theory, business informatics\r\nand management have successfully collaborated here.","lang":"eng"}],"user_id":"477","ddc":["000"]},{"external_id":{"arxiv":["2202.01651"]},"abstract":[{"lang":"eng","text":"Algorithm configuration (AC) is concerned with the automated search of the\r\nmost suitable parameter configuration of a parametrized algorithm. There is\r\ncurrently a wide variety of AC problem variants and methods proposed in the\r\nliterature. Existing reviews do not take into account all derivatives of the AC\r\nproblem, nor do they offer a complete classification scheme. To this end, we\r\nintroduce taxonomies to describe the AC problem and features of configuration\r\nmethods, respectively. We review existing AC literature within the lens of our\r\ntaxonomies, outline relevant design choices of configuration approaches,\r\ncontrast methods and problem variants against each other, and describe the\r\nstate of AC in industry. Finally, our review provides researchers and\r\npractitioners with a look at future research directions in the field of AC."}],"title":"A Survey of Methods for Automated Algorithm Configuration","user_id":"38209","author":[{"last_name":"Schede","full_name":"Schede, Elias","first_name":"Elias"},{"last_name":"Brandt","first_name":"Jasmin","full_name":"Brandt, Jasmin"},{"id":"38209","last_name":"Tornede","full_name":"Tornede, Alexander","first_name":"Alexander"},{"orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik","first_name":"Marcel Dominik","id":"33176","last_name":"Wever"},{"first_name":"Viktor","full_name":"Bengs, Viktor","last_name":"Bengs","id":"76599"},{"last_name":"Hüllermeier","id":"48129","first_name":"Eyke","full_name":"Hüllermeier, Eyke"},{"first_name":"Kevin","full_name":"Tierney, Kevin","last_name":"Tierney"}],"publication":"arXiv:2202.01651","department":[{"_id":"34"},{"_id":"7"},{"_id":"26"}],"status":"public","project":[{"_id":"1","name":"SFB 901: SFB 901"},{"name":"SFB 901 - B: SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - B2: SFB 901 - Subproject B2","_id":"10"}],"date_created":"2022-04-12T12:00:08Z","date_updated":"2022-04-12T12:01:15Z","_id":"30868","type":"preprint","citation":{"ieee":"E. Schede et al., “A Survey of Methods for Automated Algorithm Configuration,” arXiv:2202.01651. 2022.","short":"E. Schede, J. Brandt, A. Tornede, M.D. Wever, V. Bengs, E. Hüllermeier, K. Tierney, ArXiv:2202.01651 (2022).","bibtex":"@article{Schede_Brandt_Tornede_Wever_Bengs_Hüllermeier_Tierney_2022, title={A Survey of Methods for Automated Algorithm Configuration}, journal={arXiv:2202.01651}, author={Schede, Elias and Brandt, Jasmin and Tornede, Alexander and Wever, Marcel Dominik and Bengs, Viktor and Hüllermeier, Eyke and Tierney, Kevin}, year={2022} }","mla":"Schede, Elias, et al. “A Survey of Methods for Automated Algorithm Configuration.” ArXiv:2202.01651, 2022.","apa":"Schede, E., Brandt, J., Tornede, A., Wever, M. D., Bengs, V., Hüllermeier, E., & Tierney, K. (2022). A Survey of Methods for Automated Algorithm Configuration. In arXiv:2202.01651.","ama":"Schede E, Brandt J, Tornede A, et al. A Survey of Methods for Automated Algorithm Configuration. arXiv:220201651. Published online 2022.","chicago":"Schede, Elias, Jasmin Brandt, Alexander Tornede, Marcel Dominik Wever, Viktor Bengs, Eyke Hüllermeier, and Kevin Tierney. “A Survey of Methods for Automated Algorithm Configuration.” ArXiv:2202.01651, 2022."},"year":"2022","language":[{"iso":"eng"}]},{"date_updated":"2022-11-17T13:00:53Z","_id":"34103","conference":{"location":"Baltimore","name":"Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022"},"language":[{"iso":"eng"}],"type":"conference","year":"2022","citation":{"bibtex":"@inproceedings{Fehring_Hanselle_Tornede_2022, title={HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection}, booktitle={Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022}, author={Fehring, Lukass and Hanselle, Jonas Manuel and Tornede, Alexander}, year={2022} }","mla":"Fehring, Lukass, et al. “HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection.” Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022, 2022.","chicago":"Fehring, Lukass, Jonas Manuel Hanselle, and Alexander Tornede. “HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection.” In Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022, 2022.","ama":"Fehring L, Hanselle JM, Tornede A. HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection. In: Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022. ; 2022.","apa":"Fehring, L., Hanselle, J. M., & Tornede, A. (2022). HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection. Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022. Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022, Baltimore.","ieee":"L. Fehring, J. M. Hanselle, and A. Tornede, “HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection,” presented at the Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022, Baltimore, 2022.","short":"L. Fehring, J.M. Hanselle, A. Tornede, in: Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022, 2022."},"user_id":"38209","title":"HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection","external_id":{"arxiv":["2210.17341"]},"abstract":[{"text":"It is well known that different algorithms perform differently well on an\r\ninstance of an algorithmic problem, motivating algorithm selection (AS): Given\r\nan instance of an algorithmic problem, which is the most suitable algorithm to\r\nsolve it? As such, the AS problem has received considerable attention resulting\r\nin various approaches - many of which either solve a regression or ranking\r\nproblem under the hood. Although both of these formulations yield very natural\r\nways to tackle AS, they have considerable weaknesses. On the one hand,\r\ncorrectly predicting the performance of an algorithm on an instance is a\r\nsufficient, but not a necessary condition to produce a correct ranking over\r\nalgorithms and in particular ranking the best algorithm first. On the other\r\nhand, classical ranking approaches often do not account for concrete\r\nperformance values available in the training data, but only leverage rankings\r\ncomposed from such data. We propose HARRIS- Hybrid rAnking and RegRessIon\r\nforeSts - a new algorithm selector leveraging special forests, combining the\r\nstrengths of both approaches while alleviating their weaknesses. HARRIS'\r\ndecisions are based on a forest model, whose trees are created based on splits\r\noptimized on a hybrid ranking and regression loss function. As our preliminary\r\nexperimental study on ASLib shows, HARRIS improves over standard algorithm\r\nselection approaches on some scenarios showing that combining ranking and\r\nregression in trees is indeed promising for AS.","lang":"eng"}],"status":"public","project":[{"_id":"1","name":"SFB 901: SFB 901"},{"name":"SFB 901 - B: SFB 901 - Project Area B","_id":"3"},{"_id":"10","name":"SFB 901 - B2: SFB 901 - Subproject B2"}],"date_created":"2022-11-17T12:57:40Z","author":[{"full_name":"Fehring, Lukass","first_name":"Lukass","last_name":"Fehring"},{"first_name":"Jonas Manuel","orcid":"0000-0002-1231-4985","full_name":"Hanselle, Jonas Manuel","last_name":"Hanselle","id":"43980"},{"full_name":"Tornede, Alexander","first_name":"Alexander","id":"38209","last_name":"Tornede"}],"publication":"Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022"},{"status":"public","project":[{"name":"SFB 901: SFB 901","_id":"1"},{"name":"SFB 901 - B: SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - B2: SFB 901 - Subproject B2","_id":"10"}],"date_created":"2022-06-08T08:47:33Z","author":[{"last_name":"Dreßler","id":"78256","first_name":"Kevin","full_name":"Dreßler, Kevin"},{"full_name":"Sherif, Mohamed","first_name":"Mohamed","id":"67234","last_name":"Sherif"},{"last_name":"Ngonga Ngomo","id":"65716","first_name":"Axel-Cyrille","full_name":"Ngonga Ngomo, Axel-Cyrille"}],"keyword":["2022 RAKI SFB901 deer dice kevin knowgraphs limes ngonga sherif simba"],"department":[{"_id":"34"}],"publication":"Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia","ddc":["000"],"title":"ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-expression Learning","user_id":"477","abstract":[{"text":"The creation of an RDF knowledge graph for a particular application commonly involves a pipeline of tools that transform a set ofinput data sources into an RDF knowledge graph in a process called dataset augmentation. The components of such augmentation pipelines often require extensive configuration to lead to satisfactory results. Thus, non-experts are often unable to use them. Wepresent an efficient supervised algorithm based on genetic programming for learning knowledge graph augmentation pipelines of arbitrary length. Our approach uses multi-expression learning to learn augmentation pipelines able to achieve a high F-measure on the training data. Our evaluation suggests that our approach can efficiently learn a larger class of RDF dataset augmentation tasks than the state of the art while using only a single training example. Even on the most complex augmentation problem we posed, our approach consistently achieves an average F1-measure of 99% in under 500 iterations with an average runtime of 16 seconds","lang":"eng"}],"citation":{"ama":"Dreßler K, Sherif M, Ngonga Ngomo A-C. ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-expression Learning. In: Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia. ; 2022. doi:10.1145/3511095.3531287","apa":"Dreßler, K., Sherif, M., & Ngonga Ngomo, A.-C. (2022). ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-expression Learning. Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia. HT ’22: 33rd ACM Conference on Hypertext and Social Media, Barcelona (Spain). https://doi.org/10.1145/3511095.3531287","chicago":"Dreßler, Kevin, Mohamed Sherif, and Axel-Cyrille Ngonga Ngomo. “ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-Expression Learning.” In Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia, 2022. https://doi.org/10.1145/3511095.3531287.","bibtex":"@inproceedings{Dreßler_Sherif_Ngonga Ngomo_2022, title={ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-expression Learning}, DOI={10.1145/3511095.3531287}, booktitle={Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia}, author={Dreßler, Kevin and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}, year={2022} }","mla":"Dreßler, Kevin, et al. “ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-Expression Learning.” Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia, 2022, doi:10.1145/3511095.3531287.","short":"K. Dreßler, M. Sherif, A.-C. Ngonga Ngomo, in: Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia, 2022.","ieee":"K. Dreßler, M. Sherif, and A.-C. Ngonga Ngomo, “ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-expression Learning,” presented at the HT ’22: 33rd ACM Conference on Hypertext and Social Media, Barcelona (Spain), 2022, doi: 10.1145/3511095.3531287."},"type":"conference","year":"2022","language":[{"iso":"eng"}],"doi":"10.1145/3511095.3531287","_id":"31806","date_updated":"2022-11-18T10:11:38Z","conference":{"end_date":"2022-07-01","start_date":"2022-06-28","name":"HT ’22: 33rd ACM Conference on Hypertext and Social Media","location":"Barcelona (Spain)"}},{"oa":"1","date_updated":"2022-08-20T07:02:04Z","_id":"33033","citation":{"ieee":"L. Fehring, Combined Ranking and Regression Trees for Algorithm Selection. Paderborn, 2022.","short":"L. Fehring, Combined Ranking and Regression Trees for Algorithm Selection, Paderborn, 2022.","bibtex":"@book{Fehring_2022, place={Paderborn}, title={Combined Ranking and Regression Trees for Algorithm Selection}, author={Fehring, Lukas}, year={2022} }","mla":"Fehring, Lukas. Combined Ranking and Regression Trees for Algorithm Selection. 2022.","chicago":"Fehring, Lukas. Combined Ranking and Regression Trees for Algorithm Selection. Paderborn, 2022.","ama":"Fehring L. Combined Ranking and Regression Trees for Algorithm Selection.; 2022.","apa":"Fehring, L. (2022). Combined Ranking and Regression Trees for Algorithm Selection."},"type":"bachelorsthesis","year":"2022","language":[{"iso":"eng"}],"title":"Combined Ranking and Regression Trees for Algorithm Selection","ddc":["006"],"user_id":"38209","place":"Paderborn","has_accepted_license":"1","status":"public","project":[{"_id":"1","name":"SFB 901: SFB 901"},{"_id":"3","name":"SFB 901 - B: SFB 901 - Project Area B"},{"_id":"10","name":"SFB 901 - B2: SFB 901 - Subproject B2"}],"date_created":"2022-08-19T09:41:14Z","author":[{"last_name":"Fehring","id":"75695","first_name":"Lukas","full_name":"Fehring, Lukas"}],"file_date_updated":"2022-08-19T09:39:57Z","department":[{"_id":"34"},{"_id":"7"},{"_id":"26"}],"file":[{"relation":"main_file","content_type":"application/pdf","date_updated":"2022-08-19T09:39:57Z","creator":"ahetzer","file_id":"33034","file_size":24830795,"access_level":"open_access","file_name":"Final Bachelor Thesis.pdf","date_created":"2022-08-19T09:39:57Z"}]},{"date_updated":"2022-08-24T12:44:27Z","_id":"30867","language":[{"iso":"eng"}],"citation":{"bibtex":"@article{Tornede_Bengs_Hüllermeier_2022, title={Machine Learning for Online Algorithm Selection under Censored Feedback}, journal={Proceedings of the 36th AAAI Conference on Artificial Intelligence}, publisher={AAAI}, author={Tornede, Alexander and Bengs, Viktor and Hüllermeier, Eyke}, year={2022} }","mla":"Tornede, Alexander, et al. “Machine Learning for Online Algorithm Selection under Censored Feedback.” Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI, 2022.","chicago":"Tornede, Alexander, Viktor Bengs, and Eyke Hüllermeier. “Machine Learning for Online Algorithm Selection under Censored Feedback.” Proceedings of the 36th AAAI Conference on Artificial Intelligence. AAAI, 2022.","apa":"Tornede, A., Bengs, V., & Hüllermeier, E. (2022). Machine Learning for Online Algorithm Selection under Censored Feedback. In Proceedings of the 36th AAAI Conference on Artificial Intelligence. AAAI.","ama":"Tornede A, Bengs V, Hüllermeier E. Machine Learning for Online Algorithm Selection under Censored Feedback. Proceedings of the 36th AAAI Conference on Artificial Intelligence. Published online 2022.","ieee":"A. Tornede, V. Bengs, and E. Hüllermeier, “Machine Learning for Online Algorithm Selection under Censored Feedback,” Proceedings of the 36th AAAI Conference on Artificial Intelligence. AAAI, 2022.","short":"A. Tornede, V. Bengs, E. Hüllermeier, Proceedings of the 36th AAAI Conference on Artificial Intelligence (2022)."},"year":"2022","type":"preprint","user_id":"38209","title":"Machine Learning for Online Algorithm Selection under Censored Feedback","abstract":[{"lang":"eng","text":"In online algorithm selection (OAS), instances of an algorithmic problem\r\nclass are presented to an agent one after another, and the agent has to quickly\r\nselect a presumably best algorithm from a fixed set of candidate algorithms.\r\nFor decision problems such as satisfiability (SAT), quality typically refers to\r\nthe algorithm's runtime. As the latter is known to exhibit a heavy-tail\r\ndistribution, an algorithm is normally stopped when exceeding a predefined\r\nupper time limit. As a consequence, machine learning methods used to optimize\r\nan algorithm selection strategy in a data-driven manner need to deal with\r\nright-censored samples, a problem that has received little attention in the\r\nliterature so far. In this work, we revisit multi-armed bandit algorithms for\r\nOAS and discuss their capability of dealing with the problem. Moreover, we\r\nadapt them towards runtime-oriented losses, allowing for partially censored\r\ndata while keeping a space- and time-complexity independent of the time\r\nhorizon. In an extensive experimental evaluation on an adapted version of the\r\nASlib benchmark, we demonstrate that theoretically well-founded methods based\r\non Thompson sampling perform specifically strong and improve in comparison to\r\nexisting methods."}],"external_id":{"arxiv":["2109.06234"]},"project":[{"name":"SFB 901: SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - B: SFB 901 - Project Area B"},{"_id":"10","name":"SFB 901 - B2: SFB 901 - Subproject B2"}],"date_created":"2022-04-12T11:58:56Z","status":"public","department":[{"_id":"34"},{"_id":"7"},{"_id":"26"}],"publication":"Proceedings of the 36th AAAI Conference on Artificial Intelligence","author":[{"last_name":"Tornede","id":"38209","first_name":"Alexander","full_name":"Tornede, Alexander"},{"full_name":"Bengs, Viktor","first_name":"Viktor","id":"76599","last_name":"Bengs"},{"last_name":"Hüllermeier","id":"48129","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}],"publisher":"AAAI"},{"date_updated":"2022-08-24T12:45:39Z","_id":"30865","type":"preprint","year":"2022","citation":{"ama":"Tornede A, Gehring L, Tornede T, Wever MD, Hüllermeier E. Algorithm Selection on a Meta Level. Machine Learning. Published online 2022.","apa":"Tornede, A., Gehring, L., Tornede, T., Wever, M. D., & Hüllermeier, E. (2022). Algorithm Selection on a Meta Level. In Machine Learning.","chicago":"Tornede, Alexander, Lukas Gehring, Tanja Tornede, Marcel Dominik Wever, and Eyke Hüllermeier. “Algorithm Selection on a Meta Level.” Machine Learning, 2022.","bibtex":"@article{Tornede_Gehring_Tornede_Wever_Hüllermeier_2022, title={Algorithm Selection on a Meta Level}, journal={Machine Learning}, author={Tornede, Alexander and Gehring, Lukas and Tornede, Tanja and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2022} }","mla":"Tornede, Alexander, et al. “Algorithm Selection on a Meta Level.” Machine Learning, 2022.","short":"A. Tornede, L. Gehring, T. Tornede, M.D. Wever, E. Hüllermeier, Machine Learning (2022).","ieee":"A. Tornede, L. Gehring, T. Tornede, M. D. Wever, and E. Hüllermeier, “Algorithm Selection on a Meta Level,” Machine Learning. 2022."},"language":[{"iso":"eng"}],"abstract":[{"lang":"eng","text":"The problem of selecting an algorithm that appears most suitable for a\r\nspecific instance of an algorithmic problem class, such as the Boolean\r\nsatisfiability problem, is called instance-specific algorithm selection. Over\r\nthe past decade, the problem has received considerable attention, resulting in\r\na number of different methods for algorithm selection. Although most of these\r\nmethods are based on machine learning, surprisingly little work has been done\r\non meta learning, that is, on taking advantage of the complementarity of\r\nexisting algorithm selection methods in order to combine them into a single\r\nsuperior algorithm selector. In this paper, we introduce the problem of meta\r\nalgorithm selection, which essentially asks for the best way to combine a given\r\nset of algorithm selectors. We present a general methodological framework for\r\nmeta algorithm selection as well as several concrete learning methods as\r\ninstantiations of this framework, essentially combining ideas of meta learning\r\nand ensemble learning. In an extensive experimental evaluation, we demonstrate\r\nthat ensembles of algorithm selectors can significantly outperform single\r\nalgorithm selectors and have the potential to form the new state of the art in\r\nalgorithm selection."}],"external_id":{"arxiv":["2107.09414"]},"title":"Algorithm Selection on a Meta Level","user_id":"38209","author":[{"last_name":"Tornede","id":"38209","first_name":"Alexander","full_name":"Tornede, Alexander"},{"first_name":"Lukas","full_name":"Gehring, Lukas","last_name":"Gehring"},{"id":"40795","last_name":"Tornede","full_name":"Tornede, Tanja","first_name":"Tanja"},{"first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik","last_name":"Wever","id":"33176"},{"last_name":"Hüllermeier","id":"48129","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}],"publication":"Machine Learning","department":[{"_id":"34"},{"_id":"7"},{"_id":"26"}],"status":"public","date_created":"2022-04-12T11:55:18Z","project":[{"_id":"1","name":"SFB 901: SFB 901"},{"_id":"3","name":"SFB 901 - B: SFB 901 - Project Area B"},{"_id":"10","name":"SFB 901 - B2: SFB 901 - Subproject B2"}]},{"keyword":["Metals and Alloys","Mechanical Engineering","Mechanics of Materials"],"publication":"Welding in the World","publisher":"Springer Science and Business Media LLC","author":[{"last_name":"Gevers","id":"83151","first_name":"Karina","full_name":"Gevers, Karina"},{"full_name":"Tornede, Alexander","first_name":"Alexander","id":"38209","last_name":"Tornede"},{"first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik","last_name":"Wever","id":"33176"},{"first_name":"Volker","full_name":"Schöppner, Volker","last_name":"Schöppner","id":"20530"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","id":"48129"}],"project":[{"name":"SFB 901: SFB 901","_id":"1"},{"name":"SFB 901 - B: SFB 901 - Project Area B","_id":"3"},{"_id":"10","name":"SFB 901 - B2: SFB 901 - Subproject B2"}],"date_created":"2022-08-24T12:51:07Z","status":"public","publication_status":"published","publication_identifier":{"issn":["0043-2288","1878-6669"]},"abstract":[{"lang":"eng","text":"AbstractHeated tool butt welding is a method often used for joining thermoplastics, especially when the components are made out of different materials. The quality of the connection between the components crucially depends on a suitable choice of the parameters of the welding process, such as heating time, temperature, and the precise way how the parts are then welded. Moreover, when different materials are to be joined, the parameter values need to be tailored to the specifics of the respective material. To this end, in this paper, three approaches to tailor the parameter values to optimize the quality of the connection are compared: a heuristic by Potente, statistical experimental design, and Bayesian optimization. With the suitability for practice in mind, a series of experiments are carried out with these approaches, and their capabilities of proposing well-performing parameter values are investigated. As a result, Bayesian optimization is found to yield peak performance, but the costs for optimization are substantial. In contrast, the Potente heuristic does not require any experimentation and recommends parameter values with competitive quality."}],"user_id":"38209","title":"A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials","language":[{"iso":"eng"}],"type":"journal_article","citation":{"short":"K. Gevers, A. Tornede, M.D. Wever, V. Schöppner, E. Hüllermeier, Welding in the World (2022).","ieee":"K. Gevers, A. Tornede, M. D. Wever, V. Schöppner, and E. Hüllermeier, “A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials,” Welding in the World, 2022, doi: 10.1007/s40194-022-01339-9.","chicago":"Gevers, Karina, Alexander Tornede, Marcel Dominik Wever, Volker Schöppner, and Eyke Hüllermeier. “A Comparison of Heuristic, Statistical, and Machine Learning Methods for Heated Tool Butt Welding of Two Different Materials.” Welding in the World, 2022. https://doi.org/10.1007/s40194-022-01339-9.","ama":"Gevers K, Tornede A, Wever MD, Schöppner V, Hüllermeier E. A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials. Welding in the World. Published online 2022. doi:10.1007/s40194-022-01339-9","apa":"Gevers, K., Tornede, A., Wever, M. D., Schöppner, V., & Hüllermeier, E. (2022). A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials. Welding in the World. https://doi.org/10.1007/s40194-022-01339-9","mla":"Gevers, Karina, et al. “A Comparison of Heuristic, Statistical, and Machine Learning Methods for Heated Tool Butt Welding of Two Different Materials.” Welding in the World, Springer Science and Business Media LLC, 2022, doi:10.1007/s40194-022-01339-9.","bibtex":"@article{Gevers_Tornede_Wever_Schöppner_Hüllermeier_2022, title={A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials}, DOI={10.1007/s40194-022-01339-9}, journal={Welding in the World}, publisher={Springer Science and Business Media LLC}, author={Gevers, Karina and Tornede, Alexander and Wever, Marcel Dominik and Schöppner, Volker and Hüllermeier, Eyke}, year={2022} }"},"year":"2022","_id":"33090","date_updated":"2022-08-24T12:52:06Z","doi":"10.1007/s40194-022-01339-9"}]