ORCID: https://orcid.org/0000-0002-2415-2186; Wever, Marcel
ORCID: https://orcid.org/0000-0001-9782-6818; Werner, Stefan; Mohr, Felix und Hüllermeier, Eyke
ORCID: https://orcid.org/0000-0002-9944-4108
(November 2020):
Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis.
12th Asian Conference on Machine Learning (ACML 2020), Bangkok, Thailand, 18-20 November 2020.
Jialin Pan, Sinno und Sugiyama, Masashi (eds.) :
In: Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020),
Vol. 129
pp. 737-752
[PDF, 390kB]

Abstract
Algorithm selection (AS) deals with the automatic selection of an algorithm from a fixed set of candidate algorithms most suitable for a specific instance of an algorithmic problem class, where "suitability" often refers to an algorithm's runtime. Due to possibly extremely long runtimes of candidate algorithms, training data for algorithm selection models is usually generated under time constraints in the sense that not all algorithms are run to completion on all instances. Thus, training data usually comprises censored information, as the true runtime of algorithms timed out remains unknown. However, many standard AS approaches are not able to handle such information in a proper way. On the other side, survival analysis (SA) naturally supports censored data and offers appropriate ways to use such data for learning distributional models of algorithm runtime, as we demonstrate in this work. We leverage such models as a basis of a sophisticated decision-theoretic approach to algorithm selection, which we dub Run2Survive. Moreover, taking advantage of a framework of this kind, we advocate a risk-averse approach to algorithm selection, in which the avoidance of a timeout is given high priority. In an extensive experimental study with the standard benchmark ASlib, our approach is shown to be highly competitive and in many cases even superior to state-of-the-art AS approaches.
Item Type: | Conference or Workshop Item (Paper) |
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Faculties: | Mathematics, Computer Science and Statistics > Computer Science > Artificial Intelligence and Machine Learning |
Subjects: | 000 Computer science, information and general works > 004 Data processing computer science |
URN: | urn:nbn:de:bvb:19-epub-91648-7 |
Language: | English |
Item ID: | 91648 |
Date Deposited: | 28. Mar 2022 12:41 |
Last Modified: | 04. Dec 2024 08:14 |