{"citation":{"mla":"Tornede, Alexander, et al. “Run2Survive: A Decision-Theoretic Approach to Algorithm Selection Based on Survival Analysis.” <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.","ieee":"A. Tornede, M. D. Wever, S. Werner, F. Mohr, and E. Hüllermeier, “Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis,” presented at the 12th Asian Conference on Machine Learning, Bangkok, Thailand, 2020.","apa":"Tornede, A., Wever, M. D., Werner, S., Mohr, F., &#38; Hüllermeier, E. (2020). Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. <i>ACML 2020</i>. 12th Asian Conference on Machine Learning, Bangkok, Thailand.","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.","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."},"user_id":"5786","date_created":"2020-08-25T12:09:28Z","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"_id":"18276","title":"Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis","author":[{"first_name":"Alexander","last_name":"Tornede","id":"38209","full_name":"Tornede, Alexander"},{"orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik","last_name":"Wever","id":"33176","full_name":"Wever, Marcel Dominik"},{"full_name":"Werner, Stefan","last_name":"Werner","first_name":"Stefan"},{"first_name":"Felix","last_name":"Mohr","full_name":"Mohr, Felix"},{"last_name":"Hüllermeier","id":"48129","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"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."}],"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"}],"status":"public","year":"2020","publication":"ACML 2020","conference":{"location":"Bangkok, Thailand","end_date":"2020-11-20","start_date":"2020-11-18","name":"12th Asian Conference on Machine Learning"},"type":"conference","date_updated":"2022-01-06T06:53:28Z","language":[{"iso":"eng"}],"main_file_link":[{"url":"https://arxiv.org/pdf/2007.02816.pdf"}]}