[{"year":"2023","citation":{"apa":"Hanselle, J. M., Hüllermeier, E., Mohr, F., Ngonga Ngomo, A.-C., Sherif, M., Tornede, A., &#38; Wever, M. D. (2023). Configuration and Evaluation. In C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, &#38; H. Wehrheim (Eds.), <i>On-The-Fly Computing – Individualized IT-services in dynamic markets</i> (Vol. 412, pp. 85–104). Heinz Nixdorf Institut, Universität Paderborn. <a href=\"https://doi.org/10.5281/zenodo.8068466\">https://doi.org/10.5281/zenodo.8068466</a>","mla":"Hanselle, Jonas Manuel, et al. “Configuration and Evaluation.” <i>On-The-Fly Computing – Individualized IT-Services in Dynamic Markets</i>, edited by Claus-Jochen Haake et al., vol. 412, Heinz Nixdorf Institut, Universität Paderborn, 2023, pp. 85–104, doi:<a href=\"https://doi.org/10.5281/zenodo.8068466\">10.5281/zenodo.8068466</a>.","bibtex":"@inbook{Hanselle_Hüllermeier_Mohr_Ngonga Ngomo_Sherif_Tornede_Wever_2023, series={Verlagsschriftenreihe des Heinz Nixdorf Instituts}, title={Configuration and Evaluation}, volume={412}, DOI={<a href=\"https://doi.org/10.5281/zenodo.8068466\">10.5281/zenodo.8068466</a>}, 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, 2023, pp. 85–104.","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. <i>On-The-Fly Computing – Individualized IT-Services in Dynamic Markets</i>. Vol 412. Verlagsschriftenreihe des Heinz Nixdorf Instituts. Heinz Nixdorf Institut, Universität Paderborn; 2023:85–104. doi:<a href=\"https://doi.org/10.5281/zenodo.8068466\">10.5281/zenodo.8068466</a>","ieee":"J. M. Hanselle <i>et al.</i>, “Configuration and Evaluation,” in <i>On-The-Fly Computing – Individualized IT-services in dynamic markets</i>, vol. 412, C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, and H. Wehrheim, Eds. Heinz Nixdorf Institut, Universität Paderborn, 2023, pp. 85–104.","chicago":"Hanselle, Jonas Manuel, Eyke Hüllermeier, Felix Mohr, Axel-Cyrille Ngonga Ngomo, Mohamed Sherif, Alexander Tornede, and Marcel Dominik Wever. “Configuration and Evaluation.” In <i>On-The-Fly Computing – Individualized IT-Services in Dynamic Markets</i>, 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. Heinz Nixdorf Institut, Universität Paderborn, 2023. <a href=\"https://doi.org/10.5281/zenodo.8068466\">https://doi.org/10.5281/zenodo.8068466</a>."},"intvolume":"       412","page":"85–104","title":"Configuration and Evaluation","doi":"10.5281/zenodo.8068466","date_updated":"2024-06-04T15:56:45Z","publisher":"Heinz Nixdorf Institut, Universität Paderborn","date_created":"2024-06-04T15:55:56Z","author":[{"first_name":"Jonas Manuel","id":"43980","full_name":"Hanselle, Jonas Manuel","last_name":"Hanselle","orcid":"0000-0002-1231-4985"},{"id":"48129","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","first_name":"Eyke"},{"first_name":"Felix","last_name":"Mohr","full_name":"Mohr, Felix"},{"id":"65716","full_name":"Ngonga Ngomo, Axel-Cyrille","last_name":"Ngonga Ngomo","first_name":"Axel-Cyrille"},{"first_name":"Mohamed","full_name":"Sherif, Mohamed","id":"67234","last_name":"Sherif","orcid":"https://orcid.org/0000-0002-9927-2203"},{"id":"38209","full_name":"Tornede, Alexander","last_name":"Tornede","first_name":"Alexander"},{"last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818","id":"33176","full_name":"Wever, Marcel Dominik","first_name":"Marcel Dominik"}],"volume":412,"editor":[{"last_name":"Haake","full_name":"Haake, Claus-Jochen","first_name":"Claus-Jochen"},{"full_name":"Meyer auf der Heide, Friedhelm","last_name":"Meyer auf der Heide","first_name":"Friedhelm"},{"full_name":"Platzner, Marco","last_name":"Platzner","first_name":"Marco"},{"last_name":"Wachsmuth","full_name":"Wachsmuth, Henning","first_name":"Henning"},{"first_name":"Heike","last_name":"Wehrheim","full_name":"Wehrheim, Heike"}],"status":"public","type":"book_chapter","publication":"On-The-Fly Computing – Individualized IT-services in dynamic markets","keyword":["dice ngonga sfb901 sherif"],"language":[{"iso":"eng"}],"_id":"54613","user_id":"67199","series_title":"Verlagsschriftenreihe des Heinz Nixdorf Instituts","department":[{"_id":"574"}]},{"publication":"On-The-Fly Computing -- Individualized IT-services in dynamic markets","file":[{"date_updated":"2023-07-07T11:20:11Z","date_created":"2023-07-07T07:50:34Z","creator":"florida","file_size":895091,"file_name":"B2-Chapter-SFB-Buch-Final.pdf","access_level":"open_access","file_id":"45885","content_type":"application/pdf","relation":"main_file"}],"language":[{"iso":"eng"}],"ddc":["040"],"year":"2023","date_created":"2023-07-07T07:50:53Z","publisher":"Heinz Nixdorf Institut, Universität Paderborn","title":"Configuration and Evaluation","type":"book_chapter","status":"public","editor":[{"first_name":"Claus-Jochen","last_name":"Haake","full_name":"Haake, Claus-Jochen"},{"full_name":"Meyer auf der Heide, Friedhelm","last_name":"Meyer auf der Heide","first_name":"Friedhelm"},{"first_name":"Marco","last_name":"Platzner","full_name":"Platzner, Marco"},{"last_name":"Wachsmuth","full_name":"Wachsmuth, Henning","first_name":"Henning"},{"first_name":"Heike","last_name":"Wehrheim","full_name":"Wehrheim, Heike"}],"department":[{"_id":"7"}],"user_id":"477","series_title":"Verlagsschriftenreihe des Heinz Nixdorf Instituts","_id":"45884","project":[{"grant_number":"160364472","_id":"1","name":"SFB 901: SFB 901: On-The-Fly Computing - Individualisierte IT-Dienstleistungen in dynamischen Märkten "},{"name":"SFB 901 - B: SFB 901 - Project Area B","_id":"3"},{"grant_number":"160364472","name":"SFB 901 - B2: Konfiguration und Bewertung (B02)","_id":"10"}],"file_date_updated":"2023-07-07T11:20:11Z","has_accepted_license":"1","page":"85-104","intvolume":"       412","citation":{"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. <i>On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets</i>. Vol 412. Verlagsschriftenreihe des Heinz Nixdorf Instituts. Heinz Nixdorf Institut, Universität Paderborn; 2023:85-104. doi:<a href=\"https://doi.org/10.5281/zenodo.8068466\">10.5281/zenodo.8068466</a>","ieee":"J. M. Hanselle <i>et al.</i>, “Configuration and Evaluation,” in <i>On-The-Fly Computing -- Individualized IT-services in dynamic markets</i>, 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.","chicago":"Hanselle, Jonas Manuel, Eyke Hüllermeier, Felix Mohr, Axel-Cyrille Ngonga Ngomo, Mohamed Sherif, Alexander Tornede, and Marcel Dominik Wever. “Configuration and Evaluation.” In <i>On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets</i>, 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. <a href=\"https://doi.org/10.5281/zenodo.8068466\">https://doi.org/10.5281/zenodo.8068466</a>.","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={<a href=\"https://doi.org/10.5281/zenodo.8068466\">10.5281/zenodo.8068466</a>}, 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.","mla":"Hanselle, Jonas Manuel, et al. “Configuration and Evaluation.” <i>On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets</i>, edited by Claus-Jochen Haake et al., vol. 412, Heinz Nixdorf Institut, Universität Paderborn, 2023, pp. 85–104, doi:<a href=\"https://doi.org/10.5281/zenodo.8068466\">10.5281/zenodo.8068466</a>.","apa":"Hanselle, J. M., Hüllermeier, E., Mohr, F., Ngonga Ngomo, A.-C., Sherif, M., Tornede, A., &#38; Wever, M. D. (2023). Configuration and Evaluation. In C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, &#38; H. Wehrheim (Eds.), <i>On-The-Fly Computing -- Individualized IT-services in dynamic markets</i> (Vol. 412, pp. 85–104). Heinz Nixdorf Institut, Universität Paderborn. <a href=\"https://doi.org/10.5281/zenodo.8068466\">https://doi.org/10.5281/zenodo.8068466</a>"},"place":"Paderborn","volume":412,"author":[{"first_name":"Jonas Manuel","id":"43980","full_name":"Hanselle, Jonas Manuel","orcid":"0000-0002-1231-4985","last_name":"Hanselle"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","id":"48129","last_name":"Hüllermeier"},{"first_name":"Felix","last_name":"Mohr","full_name":"Mohr, Felix"},{"first_name":"Axel-Cyrille","id":"65716","full_name":"Ngonga Ngomo, Axel-Cyrille","last_name":"Ngonga Ngomo"},{"first_name":"Mohamed","last_name":"Sherif","orcid":"https://orcid.org/0000-0002-9927-2203","full_name":"Sherif, Mohamed","id":"67234"},{"full_name":"Tornede, Alexander","id":"38209","last_name":"Tornede","first_name":"Alexander"},{"id":"33176","full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","first_name":"Marcel Dominik"}],"date_updated":"2023-07-07T11:20:12Z","oa":"1","doi":"10.5281/zenodo.8068466"},{"type":"dissertation","file":[{"access_level":"open_access","file_id":"46118","file_name":"dissertation_alexander_tornede_final_publishing_compressed.pdf","file_size":4300633,"title":" Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions","date_created":"2023-07-24T08:40:35Z","creator":"ahetzer","date_updated":"2023-07-24T08:42:01Z","relation":"main_file","content_type":"application/pdf"}],"status":"public","user_id":"15504","department":[{"_id":"355"}],"project":[{"grant_number":"160364472","_id":"10","name":"SFB 901 - B2: Konfiguration und Bewertung (B02)"},{"name":"SFB 901 - B: SFB 901 - Project Area B","_id":"3"},{"grant_number":"160364472","name":"SFB 901: SFB 901: On-The-Fly Computing - Individualisierte IT-Dienstleistungen in dynamischen Märkten ","_id":"1"}],"_id":"45780","file_date_updated":"2023-07-24T08:42:01Z","language":[{"iso":"eng"}],"ddc":["006"],"has_accepted_license":"1","citation":{"apa":"Tornede, A. (2023). <i>Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions</i>. <a href=\"https://doi.org/10.17619/UNIPB/1-1780 \">https://doi.org/10.17619/UNIPB/1-1780 </a>","ama":"Tornede A. <i>Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions</i>.; 2023. doi:<a href=\"https://doi.org/10.17619/UNIPB/1-1780 \">10.17619/UNIPB/1-1780 </a>","mla":"Tornede, Alexander. <i>Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions</i>. 2023, doi:<a href=\"https://doi.org/10.17619/UNIPB/1-1780 \">10.17619/UNIPB/1-1780 </a>.","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={<a href=\"https://doi.org/10.17619/UNIPB/1-1780 \">10.17619/UNIPB/1-1780 </a>}, author={Tornede, Alexander}, year={2023} }","short":"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. <i>Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions</i>, 2023. <a href=\"https://doi.org/10.17619/UNIPB/1-1780 \">https://doi.org/10.17619/UNIPB/1-1780 </a>.","ieee":"A. Tornede, <i>Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions</i>. 2023."},"year":"2023","supervisor":[{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier"}],"author":[{"first_name":"Alexander","full_name":"Tornede, Alexander","id":"38209","last_name":"Tornede"}],"date_created":"2023-06-27T05:20:14Z","oa":"1","date_updated":"2023-08-04T06:01:49Z","doi":"10.17619/UNIPB/1-1780 ","title":"Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions"},{"type":"preprint","publication":"arXiv:2202.01651","status":"public","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."}],"user_id":"38209","department":[{"_id":"34"},{"_id":"7"},{"_id":"26"}],"project":[{"_id":"1","name":"SFB 901: SFB 901"},{"_id":"3","name":"SFB 901 - B: SFB 901 - Project Area B"},{"name":"SFB 901 - B2: SFB 901 - Subproject B2","_id":"10"}],"_id":"30868","external_id":{"arxiv":["2202.01651"]},"language":[{"iso":"eng"}],"citation":{"mla":"Schede, Elias, et al. “A Survey of Methods for Automated Algorithm Configuration.” <i>ArXiv:2202.01651</i>, 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} }","apa":"Schede, E., Brandt, J., Tornede, A., Wever, M. D., Bengs, V., Hüllermeier, E., &#38; Tierney, K. (2022). A Survey of Methods for Automated Algorithm Configuration. In <i>arXiv:2202.01651</i>.","ama":"Schede E, Brandt J, Tornede A, et al. A Survey of Methods for Automated Algorithm Configuration. <i>arXiv:220201651</i>. Published online 2022.","ieee":"E. Schede <i>et al.</i>, “A Survey of Methods for Automated Algorithm Configuration,” <i>arXiv:2202.01651</i>. 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.” <i>ArXiv:2202.01651</i>, 2022."},"year":"2022","date_created":"2022-04-12T12:00:08Z","author":[{"full_name":"Schede, Elias","last_name":"Schede","first_name":"Elias"},{"last_name":"Brandt","full_name":"Brandt, Jasmin","first_name":"Jasmin"},{"last_name":"Tornede","id":"38209","full_name":"Tornede, Alexander","first_name":"Alexander"},{"id":"33176","full_name":"Wever, Marcel Dominik","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik"},{"last_name":"Bengs","id":"76599","full_name":"Bengs, Viktor","first_name":"Viktor"},{"last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","id":"48129","first_name":"Eyke"},{"first_name":"Kevin","last_name":"Tierney","full_name":"Tierney, Kevin"}],"date_updated":"2022-04-12T12:01:15Z","title":"A Survey of Methods for Automated Algorithm Configuration"},{"publication":"Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022","type":"conference","status":"public","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"}],"user_id":"38209","_id":"34103","external_id":{"arxiv":["2210.17341"]},"project":[{"name":"SFB 901: SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - B: SFB 901 - Project Area B"},{"name":"SFB 901 - B2: SFB 901 - Subproject B2","_id":"10"}],"language":[{"iso":"eng"}],"citation":{"ama":"Fehring L, Hanselle JM, Tornede A. HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection. In: <i>Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022</i>. ; 2022.","chicago":"Fehring, Lukass, Jonas Manuel Hanselle, and Alexander Tornede. “HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection.” In <i>Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022</i>, 2022.","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.","apa":"Fehring, L., Hanselle, J. M., &#38; Tornede, A. (2022). HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection. <i>Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022</i>. Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022, Baltimore.","mla":"Fehring, Lukass, et al. “HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection.” <i>Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022</i>, 2022.","short":"L. Fehring, J.M. Hanselle, A. Tornede, in: Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022, 2022.","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} }"},"year":"2022","date_created":"2022-11-17T12:57:40Z","author":[{"full_name":"Fehring, Lukass","last_name":"Fehring","first_name":"Lukass"},{"last_name":"Hanselle","orcid":"0000-0002-1231-4985","id":"43980","full_name":"Hanselle, Jonas Manuel","first_name":"Jonas Manuel"},{"id":"38209","full_name":"Tornede, Alexander","last_name":"Tornede","first_name":"Alexander"}],"date_updated":"2022-11-17T13:00:53Z","conference":{"location":"Baltimore","name":"Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022"},"title":"HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection"},{"status":"public","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."}],"publication":"Proceedings of the 36th AAAI Conference on Artificial Intelligence","type":"preprint","language":[{"iso":"eng"}],"department":[{"_id":"34"},{"_id":"7"},{"_id":"26"}],"user_id":"38209","external_id":{"arxiv":["2109.06234"]},"_id":"30867","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"}],"citation":{"apa":"Tornede, A., Bengs, V., &#38; Hüllermeier, E. (2022). Machine Learning for Online Algorithm Selection under Censored Feedback. In <i>Proceedings of the 36th AAAI Conference on Artificial Intelligence</i>. AAAI.","mla":"Tornede, Alexander, et al. “Machine Learning for Online Algorithm Selection under Censored Feedback.” <i>Proceedings of the 36th AAAI Conference on Artificial Intelligence</i>, AAAI, 2022.","short":"A. Tornede, V. Bengs, E. Hüllermeier, Proceedings of the 36th AAAI Conference on Artificial Intelligence (2022).","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} }","chicago":"Tornede, Alexander, Viktor Bengs, and Eyke Hüllermeier. “Machine Learning for Online Algorithm Selection under Censored Feedback.” <i>Proceedings of the 36th AAAI Conference on Artificial Intelligence</i>. AAAI, 2022.","ieee":"A. Tornede, V. Bengs, and E. Hüllermeier, “Machine Learning for Online Algorithm Selection under Censored Feedback,” <i>Proceedings of the 36th AAAI Conference on Artificial Intelligence</i>. AAAI, 2022.","ama":"Tornede A, Bengs V, Hüllermeier E. Machine Learning for Online Algorithm Selection under Censored Feedback. <i>Proceedings of the 36th AAAI Conference on Artificial Intelligence</i>. Published online 2022."},"year":"2022","title":"Machine Learning for Online Algorithm Selection under Censored Feedback","author":[{"full_name":"Tornede, Alexander","id":"38209","last_name":"Tornede","first_name":"Alexander"},{"full_name":"Bengs, Viktor","id":"76599","last_name":"Bengs","first_name":"Viktor"},{"first_name":"Eyke","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","id":"48129"}],"date_created":"2022-04-12T11:58:56Z","date_updated":"2022-08-24T12:44:27Z","publisher":"AAAI"},{"language":[{"iso":"eng"}],"user_id":"38209","department":[{"_id":"34"},{"_id":"7"},{"_id":"26"}],"project":[{"name":"SFB 901: SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - B: SFB 901 - Project Area B"},{"name":"SFB 901 - B2: SFB 901 - Subproject B2","_id":"10"}],"external_id":{"arxiv":["2107.09414"]},"_id":"30865","status":"public","abstract":[{"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.","lang":"eng"}],"type":"preprint","publication":"Machine Learning","title":"Algorithm Selection on a Meta Level","date_created":"2022-04-12T11:55:18Z","author":[{"first_name":"Alexander","id":"38209","full_name":"Tornede, Alexander","last_name":"Tornede"},{"first_name":"Lukas","full_name":"Gehring, Lukas","last_name":"Gehring"},{"first_name":"Tanja","last_name":"Tornede","full_name":"Tornede, Tanja","id":"40795"},{"first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","full_name":"Wever, Marcel Dominik","id":"33176"},{"first_name":"Eyke","last_name":"Hüllermeier","id":"48129","full_name":"Hüllermeier, Eyke"}],"date_updated":"2022-08-24T12:45:39Z","citation":{"mla":"Tornede, Alexander, et al. “Algorithm Selection on a Meta Level.” <i>Machine Learning</i>, 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} }","short":"A. Tornede, L. Gehring, T. Tornede, M.D. Wever, E. Hüllermeier, Machine Learning (2022).","apa":"Tornede, A., Gehring, L., Tornede, T., Wever, M. D., &#38; Hüllermeier, E. (2022). Algorithm Selection on a Meta Level. In <i>Machine Learning</i>.","chicago":"Tornede, Alexander, Lukas Gehring, Tanja Tornede, Marcel Dominik Wever, and Eyke Hüllermeier. “Algorithm Selection on a Meta Level.” <i>Machine Learning</i>, 2022.","ieee":"A. Tornede, L. Gehring, T. Tornede, M. D. Wever, and E. Hüllermeier, “Algorithm Selection on a Meta Level,” <i>Machine Learning</i>. 2022.","ama":"Tornede A, Gehring L, Tornede T, Wever MD, Hüllermeier E. Algorithm Selection on a Meta Level. <i>Machine Learning</i>. Published online 2022."},"year":"2022"},{"doi":"10.1007/s40194-022-01339-9","title":"A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials","author":[{"first_name":"Karina","full_name":"Gevers, Karina","id":"83151","last_name":"Gevers"},{"last_name":"Tornede","id":"38209","full_name":"Tornede, Alexander","first_name":"Alexander"},{"last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818","id":"33176","full_name":"Wever, Marcel Dominik","first_name":"Marcel Dominik"},{"first_name":"Volker","id":"20530","full_name":"Schöppner, Volker","last_name":"Schöppner"},{"id":"48129","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","first_name":"Eyke"}],"date_created":"2022-08-24T12:51:07Z","publisher":"Springer Science and Business Media LLC","date_updated":"2022-08-24T12:52:06Z","citation":{"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={<a href=\"https://doi.org/10.1007/s40194-022-01339-9\">10.1007/s40194-022-01339-9</a>}, 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} }","mla":"Gevers, Karina, et al. “A Comparison of Heuristic, Statistical, and Machine Learning Methods for Heated Tool Butt Welding of Two Different Materials.” <i>Welding in the World</i>, Springer Science and Business Media LLC, 2022, doi:<a href=\"https://doi.org/10.1007/s40194-022-01339-9\">10.1007/s40194-022-01339-9</a>.","short":"K. Gevers, A. Tornede, M.D. Wever, V. Schöppner, E. Hüllermeier, Welding in the World (2022).","apa":"Gevers, K., Tornede, A., Wever, M. D., Schöppner, V., &#38; Hüllermeier, E. (2022). A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials. <i>Welding in the World</i>. <a href=\"https://doi.org/10.1007/s40194-022-01339-9\">https://doi.org/10.1007/s40194-022-01339-9</a>","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. <i>Welding in the World</i>. Published online 2022. doi:<a href=\"https://doi.org/10.1007/s40194-022-01339-9\">10.1007/s40194-022-01339-9</a>","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.” <i>Welding in the World</i>, 2022. <a href=\"https://doi.org/10.1007/s40194-022-01339-9\">https://doi.org/10.1007/s40194-022-01339-9</a>.","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,” <i>Welding in the World</i>, 2022, doi: <a href=\"https://doi.org/10.1007/s40194-022-01339-9\">10.1007/s40194-022-01339-9</a>."},"year":"2022","publication_status":"published","publication_identifier":{"issn":["0043-2288","1878-6669"]},"language":[{"iso":"eng"}],"keyword":["Metals and Alloys","Mechanical Engineering","Mechanics of Materials"],"user_id":"38209","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"}],"_id":"33090","status":"public","abstract":[{"lang":"eng","text":"<jats:title>Abstract</jats:title><jats:p>Heated 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.</jats:p>"}],"type":"journal_article","publication":"Welding in the World"},{"type":"journal_article","publication":"IEEE Transactions on Pattern Analysis and Machine Intelligence","status":"public","abstract":[{"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.","lang":"eng"}],"user_id":"5786","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"project":[{"name":"SFB 901","_id":"1"},{"_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"}],"_id":"21004","language":[{"iso":"eng"}],"keyword":["Automated Machine Learning","Multi Label Classification","Hierarchical Planning","Bayesian Optimization"],"publication_status":"published","publication_identifier":{"issn":["0162-8828","2160-9292","1939-3539"]},"citation":{"ama":"Wever MD, Tornede A, Mohr F, Hüllermeier E. AutoML for Multi-Label Classification: Overview and Empirical Evaluation. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. Published online 2021:1-1. doi:<a href=\"https://doi.org/10.1109/tpami.2021.3051276\">10.1109/tpami.2021.3051276</a>","chicago":"Wever, Marcel Dominik, Alexander Tornede, Felix Mohr, and Eyke Hüllermeier. “AutoML for Multi-Label Classification: Overview and Empirical Evaluation.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, 2021, 1–1. <a href=\"https://doi.org/10.1109/tpami.2021.3051276\">https://doi.org/10.1109/tpami.2021.3051276</a>.","ieee":"M. D. Wever, A. Tornede, F. Mohr, and E. Hüllermeier, “AutoML for Multi-Label Classification: Overview and Empirical Evaluation,” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, pp. 1–1, 2021, doi: <a href=\"https://doi.org/10.1109/tpami.2021.3051276\">10.1109/tpami.2021.3051276</a>.","apa":"Wever, M. D., Tornede, A., Mohr, F., &#38; Hüllermeier, E. (2021). AutoML for Multi-Label Classification: Overview and Empirical Evaluation. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, 1–1. <a href=\"https://doi.org/10.1109/tpami.2021.3051276\">https://doi.org/10.1109/tpami.2021.3051276</a>","bibtex":"@article{Wever_Tornede_Mohr_Hüllermeier_2021, title={AutoML for Multi-Label Classification: Overview and Empirical Evaluation}, DOI={<a href=\"https://doi.org/10.1109/tpami.2021.3051276\">10.1109/tpami.2021.3051276</a>}, 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} }","mla":"Wever, Marcel Dominik, et al. “AutoML for Multi-Label Classification: Overview and Empirical Evaluation.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, 2021, pp. 1–1, doi:<a href=\"https://doi.org/10.1109/tpami.2021.3051276\">10.1109/tpami.2021.3051276</a>.","short":"M.D. Wever, A. Tornede, F. Mohr, E. Hüllermeier, IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) 1–1."},"page":"1-1","year":"2021","author":[{"first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","full_name":"Wever, Marcel Dominik","id":"33176"},{"first_name":"Alexander","id":"38209","full_name":"Tornede, Alexander","last_name":"Tornede"},{"full_name":"Mohr, Felix","last_name":"Mohr","first_name":"Felix"},{"id":"48129","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","first_name":"Eyke"}],"date_created":"2021-01-16T14:48:13Z","date_updated":"2022-01-06T06:54:42Z","doi":"10.1109/tpami.2021.3051276","title":"AutoML for Multi-Label Classification: Overview and Empirical Evaluation"},{"publisher":"IEEE","date_updated":"2022-01-06T06:54:45Z","author":[{"full_name":"Mohr, Felix","last_name":"Mohr","first_name":"Felix"},{"first_name":"Marcel Dominik","id":"33176","full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever"},{"first_name":"Alexander","last_name":"Tornede","full_name":"Tornede, Alexander","id":"38209"},{"last_name":"Hüllermeier","id":"48129","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"date_created":"2021-01-27T13:45:52Z","title":"Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning","publication_status":"accepted","year":"2021","citation":{"ieee":"F. Mohr, M. D. Wever, A. Tornede, and E. Hüllermeier, “Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning,” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>.","chicago":"Mohr, Felix, Marcel Dominik Wever, Alexander Tornede, and Eyke Hüllermeier. “Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, n.d.","ama":"Mohr F, Wever MD, Tornede A, Hüllermeier E. Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>.","apa":"Mohr, F., Wever, M. D., Tornede, A., &#38; Hüllermeier, E. (n.d.). Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>.","short":"F. Mohr, M.D. Wever, A. Tornede, E. Hüllermeier, IEEE Transactions on Pattern Analysis and Machine Intelligence (n.d.).","mla":"Mohr, Felix, et al. “Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, IEEE.","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} }"},"_id":"21092","project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - Subproject B2","_id":"10"},{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"user_id":"5786","language":[{"iso":"eng"}],"publication":"IEEE Transactions on Pattern Analysis and Machine Intelligence","type":"journal_article","abstract":[{"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.","lang":"eng"}],"status":"public"},{"citation":{"apa":"Tornede, T., Tornede, A., Wever, M. D., &#38; Hüllermeier, E. (2021). Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance. <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. Genetic and Evolutionary Computation Conference.","short":"T. Tornede, A. Tornede, M.D. Wever, E. Hüllermeier, in: Proceedings of the Genetic and Evolutionary Computation Conference, 2021.","mla":"Tornede, Tanja, et al. “Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2021.","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} }","ama":"Tornede T, Tornede A, Wever MD, Hüllermeier E. Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. ; 2021.","chicago":"Tornede, Tanja, Alexander Tornede, Marcel Dominik Wever, and Eyke Hüllermeier. “Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 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."},"year":"2021","author":[{"first_name":"Tanja","id":"40795","full_name":"Tornede, Tanja","last_name":"Tornede"},{"first_name":"Alexander","last_name":"Tornede","id":"38209","full_name":"Tornede, Alexander"},{"full_name":"Wever, Marcel Dominik","id":"33176","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik"},{"first_name":"Eyke","id":"48129","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier"}],"date_created":"2021-03-26T09:14:19Z","date_updated":"2022-01-06T06:55:06Z","conference":{"start_date":"2021-07-10","name":"Genetic and Evolutionary Computation Conference","end_date":"2021-07-14"},"title":"Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance","publication":"Proceedings of the Genetic and Evolutionary Computation Conference","type":"conference","status":"public","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"user_id":"5786","_id":"21570","project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Project Area B","_id":"3"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"language":[{"iso":"eng"}]},{"type":"conference","status":"public","project":[{"name":"SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - Project Area B"},{"name":"SFB 901 - Subproject B2","_id":"10"}],"_id":"22913","user_id":"5786","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"language":[{"iso":"eng"}],"quality_controlled":"1","year":"2021","citation":{"bibtex":"@inproceedings{Hüllermeier_Mohr_Tornede_Wever_2021, title={Automated Machine Learning, Bounded Rationality, and Rational Metareasoning}, author={Hüllermeier, Eyke and Mohr, Felix and Tornede, Alexander and Wever, Marcel Dominik}, year={2021} }","mla":"Hüllermeier, Eyke, et al. <i>Automated Machine Learning, Bounded Rationality, and Rational Metareasoning</i>. 2021.","short":"E. Hüllermeier, F. Mohr, A. Tornede, M.D. Wever, in: 2021.","apa":"Hüllermeier, E., Mohr, F., Tornede, A., &#38; Wever, M. D. (2021). <i>Automated Machine Learning, Bounded Rationality, and Rational Metareasoning</i>. ECML/PKDD Workshop on Automating Data Science, Bilbao (Virtual).","ama":"Hüllermeier E, Mohr F, Tornede A, Wever MD. Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. In: ; 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."},"date_updated":"2022-01-06T06:55:43Z","date_created":"2021-08-02T07:46:29Z","author":[{"id":"48129","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","first_name":"Eyke"},{"full_name":"Mohr, Felix","last_name":"Mohr","first_name":"Felix"},{"last_name":"Tornede","full_name":"Tornede, Alexander","id":"38209","first_name":"Alexander"},{"first_name":"Marcel Dominik","full_name":"Wever, Marcel Dominik","id":"33176","orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever"}],"title":"Automated Machine Learning, Bounded Rationality, and Rational Metareasoning","conference":{"start_date":"2021-09-13","name":"ECML/PKDD Workshop on Automating Data Science","location":"Bilbao (Virtual)","end_date":"2021-09-17"}},{"publication":"arXiv:2111.05850","type":"preprint","status":"public","abstract":[{"text":"Automated machine learning (AutoML) strives for the automatic configuration\r\nof machine learning algorithms and their composition into an overall (software)\r\nsolution - a machine learning pipeline - tailored to the learning task\r\n(dataset) at hand. Over the last decade, AutoML has developed into an\r\nindependent research field with hundreds of contributions. While AutoML offers\r\nmany prospects, it is also known to be quite resource-intensive, which is one\r\nof its major points of criticism. The primary cause for a high resource\r\nconsumption is that many approaches rely on the (costly) evaluation of many\r\nmachine learning pipelines while searching for good candidates. This problem is\r\namplified in the context of research on AutoML methods, due to large scale\r\nexperiments conducted with many datasets and approaches, each of them being run\r\nwith several repetitions to rule out random effects. In the spirit of recent\r\nwork on Green AI, this paper is written in an attempt to raise the awareness of\r\nAutoML researchers for the problem and to elaborate on possible remedies. To\r\nthis end, we identify four categories of actions the community may take towards\r\nmore sustainable research on AutoML, i.e. Green AutoML: design of AutoML\r\nsystems, benchmarking, transparency and research incentives.","lang":"eng"}],"department":[{"_id":"34"},{"_id":"7"},{"_id":"26"}],"user_id":"38209","external_id":{"arxiv":["2111.05850"]},"_id":"30866","project":[{"_id":"1","name":"SFB 901: SFB 901"},{"_id":"3","name":"SFB 901 - B: SFB 901 - Project Area B"},{"name":"SFB 901 - B2: SFB 901 - Subproject B2","_id":"10"}],"language":[{"iso":"eng"}],"citation":{"ama":"Tornede T, Tornede A, Hanselle JM, Wever MD, Mohr F, Hüllermeier E. Towards Green Automated Machine Learning: Status Quo and Future Directions. <i>arXiv:211105850</i>. Published online 2021.","ieee":"T. Tornede, A. Tornede, J. M. Hanselle, M. D. Wever, F. Mohr, and E. Hüllermeier, “Towards Green Automated Machine Learning: Status Quo and Future Directions,” <i>arXiv:2111.05850</i>. 2021.","chicago":"Tornede, Tanja, Alexander Tornede, Jonas Manuel Hanselle, Marcel Dominik Wever, Felix Mohr, and Eyke Hüllermeier. “Towards Green Automated Machine Learning: Status Quo and Future Directions.” <i>ArXiv:2111.05850</i>, 2021.","apa":"Tornede, T., Tornede, A., Hanselle, J. M., Wever, M. D., Mohr, F., &#38; Hüllermeier, E. (2021). Towards Green Automated Machine Learning: Status Quo and Future Directions. In <i>arXiv:2111.05850</i>.","short":"T. Tornede, A. Tornede, J.M. Hanselle, M.D. Wever, F. Mohr, E. Hüllermeier, ArXiv:2111.05850 (2021).","mla":"Tornede, Tanja, et al. “Towards Green Automated Machine Learning: Status Quo and Future Directions.” <i>ArXiv:2111.05850</i>, 2021.","bibtex":"@article{Tornede_Tornede_Hanselle_Wever_Mohr_Hüllermeier_2021, title={Towards Green Automated Machine Learning: Status Quo and Future Directions}, journal={arXiv:2111.05850}, author={Tornede, Tanja and Tornede, Alexander and Hanselle, Jonas Manuel and Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2021} }"},"year":"2021","date_created":"2022-04-12T11:57:15Z","author":[{"id":"40795","full_name":"Tornede, Tanja","last_name":"Tornede","first_name":"Tanja"},{"last_name":"Tornede","id":"38209","full_name":"Tornede, Alexander","first_name":"Alexander"},{"orcid":"0000-0002-1231-4985","last_name":"Hanselle","id":"43980","full_name":"Hanselle, Jonas Manuel","first_name":"Jonas Manuel"},{"last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818","id":"33176","full_name":"Wever, Marcel Dominik","first_name":"Marcel Dominik"},{"last_name":"Mohr","full_name":"Mohr, Felix","first_name":"Felix"},{"first_name":"Eyke","last_name":"Hüllermeier","id":"48129","full_name":"Hüllermeier, Eyke"}],"date_updated":"2022-04-12T12:01:23Z","title":"Towards Green Automated Machine Learning: Status Quo and Future Directions"},{"status":"public","type":"conference","language":[{"iso":"eng"}],"user_id":"38209","series_title":"PAKDD","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - Subproject B2","_id":"10"},{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"_id":"21198","citation":{"apa":"Hanselle, J. M., Tornede, A., Wever, M. D., &#38; Hüllermeier, E. (2021). <i>Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data</i>. The 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2021), Delhi, India.","bibtex":"@article{Hanselle_Tornede_Wever_Hüllermeier_2021, series={PAKDD}, title={Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data}, author={Hanselle, Jonas Manuel and Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2021}, collection={PAKDD} }","mla":"Hanselle, Jonas Manuel, et al. <i>Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data</i>. 2021.","short":"J.M. Hanselle, A. Tornede, M.D. Wever, E. Hüllermeier, (2021).","ama":"Hanselle JM, Tornede A, Wever MD, Hüllermeier E. Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data. Published online 2021.","chicago":"Hanselle, Jonas Manuel, Alexander Tornede, Marcel Dominik Wever, and Eyke Hüllermeier. “Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data.” PAKDD, 2021.","ieee":"J. M. Hanselle, A. Tornede, M. D. Wever, and E. Hüllermeier, “Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data.” 2021."},"year":"2021","conference":{"location":"Delhi, India","end_date":"2021-05-14","start_date":"2021-05-11","name":"The 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2021)"},"title":"Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data","author":[{"id":"43980","full_name":"Hanselle, Jonas Manuel","last_name":"Hanselle","orcid":"0000-0002-1231-4985","first_name":"Jonas Manuel"},{"first_name":"Alexander","id":"38209","full_name":"Tornede, Alexander","last_name":"Tornede"},{"orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","full_name":"Wever, Marcel Dominik","id":"33176","first_name":"Marcel Dominik"},{"last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","id":"48129","first_name":"Eyke"}],"date_created":"2021-02-09T09:30:14Z","date_updated":"2022-08-24T12:49:06Z"},{"language":[{"iso":"eng"}],"project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - Subproject B2","_id":"10"},{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"_id":"17407","user_id":"5786","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"status":"public","type":"conference","publication":"Discovery Science","title":"Extreme Algorithm Selection with Dyadic Feature Representation","conference":{"name":"Discovery Science 2020"},"date_updated":"2022-01-06T06:53:10Z","author":[{"full_name":"Tornede, Alexander","id":"38209","last_name":"Tornede","first_name":"Alexander"},{"full_name":"Wever, Marcel Dominik","id":"33176","orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","first_name":"Marcel Dominik"},{"full_name":"Hüllermeier, Eyke","id":"48129","last_name":"Hüllermeier","first_name":"Eyke"}],"date_created":"2020-07-21T10:06:51Z","year":"2020","citation":{"short":"A. Tornede, M.D. Wever, E. Hüllermeier, in: Discovery Science, 2020.","mla":"Tornede, Alexander, et al. “Extreme Algorithm Selection with Dyadic Feature Representation.” <i>Discovery Science</i>, 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} }","apa":"Tornede, A., Wever, M. D., &#38; Hüllermeier, E. (2020). Extreme Algorithm Selection with Dyadic Feature Representation. <i>Discovery Science</i>. 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.","chicago":"Tornede, Alexander, Marcel Dominik Wever, and Eyke Hüllermeier. “Extreme Algorithm Selection with Dyadic Feature Representation.” In <i>Discovery Science</i>, 2020.","ama":"Tornede A, Wever MD, Hüllermeier E. Extreme Algorithm Selection with Dyadic Feature Representation. In: <i>Discovery Science</i>. ; 2020."}},{"year":"2020","citation":{"mla":"Hanselle, Jonas Manuel, et al. “Hybrid Ranking and Regression for Algorithm Selection.” <i>KI 2020: Advances in Artificial Intelligence</i>, 2020.","short":"J.M. Hanselle, A. Tornede, M.D. Wever, E. Hüllermeier, in: KI 2020: Advances in Artificial Intelligence, 2020.","bibtex":"@inproceedings{Hanselle_Tornede_Wever_Hüllermeier_2020, title={Hybrid Ranking and Regression for Algorithm Selection}, booktitle={KI 2020: Advances in Artificial Intelligence}, author={Hanselle, Jonas Manuel and Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2020} }","apa":"Hanselle, J. M., Tornede, A., Wever, M. D., &#38; Hüllermeier, E. (2020). Hybrid Ranking and Regression for Algorithm Selection. <i>KI 2020: Advances in Artificial Intelligence</i>. 43rd German Conference on Artificial Intelligence.","ama":"Hanselle JM, Tornede A, Wever MD, Hüllermeier E. Hybrid Ranking and Regression for Algorithm Selection. In: <i>KI 2020: Advances in Artificial Intelligence</i>. ; 2020.","chicago":"Hanselle, Jonas Manuel, Alexander Tornede, Marcel Dominik Wever, and Eyke Hüllermeier. “Hybrid Ranking and Regression for Algorithm Selection.” In <i>KI 2020: Advances in Artificial Intelligence</i>, 2020.","ieee":"J. M. Hanselle, A. Tornede, M. D. Wever, and E. Hüllermeier, “Hybrid Ranking and Regression for Algorithm Selection,” presented at the 43rd German Conference on Artificial Intelligence, 2020."},"date_updated":"2022-01-06T06:53:10Z","date_created":"2020-07-21T10:21:09Z","author":[{"first_name":"Jonas Manuel","last_name":"Hanselle","orcid":"0000-0002-1231-4985","full_name":"Hanselle, Jonas Manuel","id":"43980"},{"full_name":"Tornede, Alexander","id":"38209","last_name":"Tornede","first_name":"Alexander"},{"first_name":"Marcel Dominik","id":"33176","full_name":"Wever, Marcel Dominik","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818"},{"first_name":"Eyke","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","id":"48129"}],"title":"Hybrid Ranking and Regression for Algorithm Selection","conference":{"name":"43rd German Conference on Artificial Intelligence"},"type":"conference","publication":"KI 2020: Advances in Artificial Intelligence","status":"public","project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Project Area B","_id":"3"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"_id":"17408","user_id":"5786","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"language":[{"iso":"eng"}]},{"language":[{"iso":"eng"}],"user_id":"5786","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"project":[{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"},{"_id":"3","name":"SFB 901 - Project Area B"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"name":"SFB 901","_id":"1"},{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"_id":"17424","status":"public","type":"conference","publication":"Proceedings of the ECMLPKDD 2020","conference":{"name":"IOTStream Workshop @ ECMLPKDD 2020"},"doi":"10.1007/978-3-030-66770-2_8","title":"AutoML for Predictive Maintenance: One Tool to RUL Them All","date_created":"2020-07-28T09:17:41Z","author":[{"first_name":"Tanja","full_name":"Tornede, Tanja","id":"40795","last_name":"Tornede"},{"last_name":"Tornede","id":"38209","full_name":"Tornede, Alexander","first_name":"Alexander"},{"first_name":"Marcel Dominik","full_name":"Wever, Marcel Dominik","id":"33176","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818"},{"first_name":"Felix","full_name":"Mohr, Felix","last_name":"Mohr"},{"first_name":"Eyke","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","id":"48129"}],"date_updated":"2022-01-06T06:53:11Z","citation":{"ama":"Tornede T, Tornede A, Wever MD, Mohr F, Hüllermeier E. AutoML for Predictive Maintenance: One Tool to RUL Them All. In: <i>Proceedings of the ECMLPKDD 2020</i>. ; 2020. doi:<a href=\"https://doi.org/10.1007/978-3-030-66770-2_8\">10.1007/978-3-030-66770-2_8</a>","chicago":"Tornede, Tanja, Alexander Tornede, Marcel Dominik Wever, Felix Mohr, and Eyke Hüllermeier. “AutoML for Predictive Maintenance: One Tool to RUL Them All.” In <i>Proceedings of the ECMLPKDD 2020</i>, 2020. <a href=\"https://doi.org/10.1007/978-3-030-66770-2_8\">https://doi.org/10.1007/978-3-030-66770-2_8</a>.","ieee":"T. Tornede, A. Tornede, M. D. Wever, F. Mohr, and E. Hüllermeier, “AutoML for Predictive Maintenance: One Tool to RUL Them All,” presented at the IOTStream Workshop @ ECMLPKDD 2020, 2020, doi: <a href=\"https://doi.org/10.1007/978-3-030-66770-2_8\">10.1007/978-3-030-66770-2_8</a>.","apa":"Tornede, T., Tornede, A., Wever, M. D., Mohr, F., &#38; Hüllermeier, E. (2020). AutoML for Predictive Maintenance: One Tool to RUL Them All. <i>Proceedings of the ECMLPKDD 2020</i>. IOTStream Workshop @ ECMLPKDD 2020. <a href=\"https://doi.org/10.1007/978-3-030-66770-2_8\">https://doi.org/10.1007/978-3-030-66770-2_8</a>","mla":"Tornede, Tanja, et al. “AutoML for Predictive Maintenance: One Tool to RUL Them All.” <i>Proceedings of the ECMLPKDD 2020</i>, 2020, doi:<a href=\"https://doi.org/10.1007/978-3-030-66770-2_8\">10.1007/978-3-030-66770-2_8</a>.","short":"T. Tornede, A. Tornede, M.D. Wever, F. Mohr, E. Hüllermeier, in: Proceedings of the ECMLPKDD 2020, 2020.","bibtex":"@inproceedings{Tornede_Tornede_Wever_Mohr_Hüllermeier_2020, title={AutoML for Predictive Maintenance: One Tool to RUL Them All}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-66770-2_8\">10.1007/978-3-030-66770-2_8</a>}, booktitle={Proceedings of the ECMLPKDD 2020}, author={Tornede, Tanja and Tornede, Alexander and Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2020} }"},"year":"2020"},{"user_id":"5786","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"project":[{"_id":"1","name":"SFB 901"},{"_id":"3","name":"SFB 901 - Project Area B"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"_id":"20306","language":[{"iso":"eng"}],"type":"conference","publication":"Workshop MetaLearn 2020 @ NeurIPS 2020","status":"public","date_created":"2020-11-06T09:42:27Z","author":[{"last_name":"Tornede","full_name":"Tornede, Alexander","id":"38209","first_name":"Alexander"},{"full_name":"Wever, Marcel Dominik","id":"33176","orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","first_name":"Marcel Dominik"},{"first_name":"Eyke","id":"48129","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier"}],"date_updated":"2022-01-06T06:54:26Z","conference":{"name":"Workshop MetaLearn 2020 @ NeurIPS 2020","location":"Online"},"title":"Towards Meta-Algorithm Selection","citation":{"ama":"Tornede A, Wever MD, Hüllermeier E. Towards Meta-Algorithm Selection. In: <i>Workshop MetaLearn 2020 @ NeurIPS 2020</i>. ; 2020.","ieee":"A. Tornede, M. D. Wever, and E. Hüllermeier, “Towards Meta-Algorithm Selection,” presented at the Workshop MetaLearn 2020 @ NeurIPS 2020, Online, 2020.","chicago":"Tornede, Alexander, Marcel Dominik Wever, and Eyke Hüllermeier. “Towards Meta-Algorithm Selection.” In <i>Workshop MetaLearn 2020 @ NeurIPS 2020</i>, 2020.","apa":"Tornede, A., Wever, M. D., &#38; Hüllermeier, E. (2020). Towards Meta-Algorithm Selection. <i>Workshop MetaLearn 2020 @ NeurIPS 2020</i>. Workshop MetaLearn 2020 @ NeurIPS 2020, Online.","mla":"Tornede, Alexander, et al. “Towards Meta-Algorithm Selection.” <i>Workshop MetaLearn 2020 @ NeurIPS 2020</i>, 2020.","bibtex":"@inproceedings{Tornede_Wever_Hüllermeier_2020, title={Towards Meta-Algorithm Selection}, booktitle={Workshop MetaLearn 2020 @ NeurIPS 2020}, author={Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2020} }","short":"A. Tornede, M.D. Wever, E. Hüllermeier, in: Workshop MetaLearn 2020 @ NeurIPS 2020, 2020."},"year":"2020"},{"citation":{"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.","chicago":"Tornede, Alexander, Marcel Dominik Wever, Stefan Werner, Felix Mohr, and Eyke Hüllermeier. “Run2Survive: A Decision-Theoretic Approach to Algorithm Selection Based on Survival Analysis.” In <i>ACML 2020</i>, 2020.","ama":"Tornede A, Wever MD, Werner S, Mohr F, Hüllermeier E. Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. In: <i>ACML 2020</i>. ; 2020.","apa":"Tornede, A., Wever, M. D., Werner, S., Mohr, F., &#38; Hüllermeier, E. (2020). Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. <i>ACML 2020</i>. 12th Asian Conference on Machine Learning, Bangkok, Thailand.","mla":"Tornede, Alexander, et al. “Run2Survive: A Decision-Theoretic Approach to Algorithm Selection Based on Survival Analysis.” <i>ACML 2020</i>, 2020.","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} }","short":"A. Tornede, M.D. Wever, S. Werner, F. Mohr, E. Hüllermeier, in: ACML 2020, 2020."},"year":"2020","main_file_link":[{"url":"https://arxiv.org/pdf/2007.02816.pdf"}],"conference":{"end_date":"2020-11-20","location":"Bangkok, Thailand","name":"12th Asian Conference on Machine Learning","start_date":"2020-11-18"},"title":"Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis","date_created":"2020-08-25T12:09:28Z","author":[{"first_name":"Alexander","full_name":"Tornede, Alexander","id":"38209","last_name":"Tornede"},{"last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818","id":"33176","full_name":"Wever, Marcel Dominik","first_name":"Marcel Dominik"},{"full_name":"Werner, Stefan","last_name":"Werner","first_name":"Stefan"},{"first_name":"Felix","full_name":"Mohr, Felix","last_name":"Mohr"},{"full_name":"Hüllermeier, Eyke","id":"48129","last_name":"Hüllermeier","first_name":"Eyke"}],"date_updated":"2022-01-06T06:53:28Z","status":"public","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."}],"type":"conference","publication":"ACML 2020","language":[{"iso":"eng"}],"user_id":"5786","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"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"}],"_id":"18276"},{"publication_status":"accepted","year":"2020","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.","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.","apa":"Wever, M. D., Tornede, A., Mohr, F., &#38; Hüllermeier, E. (n.d.). <i>LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification</i>. Symposium on Intelligent Data Analysis, Konstanz, Germany.","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} }","mla":"Wever, Marcel Dominik, et al. <i>LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification</i>. Springer.","short":"M.D. Wever, A. Tornede, F. Mohr, E. Hüllermeier, in: Springer, n.d."},"date_updated":"2022-01-06T06:52:30Z","publisher":"Springer","date_created":"2020-01-23T08:44:08Z","author":[{"first_name":"Marcel Dominik","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818","id":"33176","full_name":"Wever, Marcel Dominik"},{"first_name":"Alexander","id":"38209","full_name":"Tornede, Alexander","last_name":"Tornede"},{"first_name":"Felix","last_name":"Mohr","full_name":"Mohr, Felix"},{"first_name":"Eyke","last_name":"Hüllermeier","id":"48129","full_name":"Hüllermeier, Eyke"}],"title":"LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification","conference":{"name":"Symposium on Intelligent Data Analysis","start_date":"2020-04-24","end_date":"2020-04-27","location":"Konstanz, Germany"},"type":"conference","abstract":[{"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.","lang":"eng"}],"status":"public","_id":"15629","project":[{"_id":"1","name":"SFB 901"},{"_id":"3","name":"SFB 901 - Project Area B"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"user_id":"5786","language":[{"iso":"eng"}]}]