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Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis
[article]
2020
arXiv
pre-print
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We leverage such models as a basis of a sophisticated decision-theoretic approach to algorithm selection, which we dub Run2Survive. ...
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 ...
Acknowledgments This work was partially supported by the German Research Foundation (DFG) within the Collaborative Research Center "On-The-Fly Computing" (SFB 901/3 project no. 160364472). ...
arXiv:2007.02816v2
fatcat:tjk2o2dqazcn3jh657iibyu6nm
Towards Meta-Algorithm Selection
[article]
2020
arXiv
pre-print
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As an algorithm selector is again an algorithm solving a specific problem, the idea of algorithm selection could also be applied to AS algorithms, leading to a meta-AS approach: Given an instance, the ...
We elaborate on consequences of applying AS on a meta-level and identify possible problems. Empirically, we show that meta-algorithm-selection can indeed prove beneficial in some cases. ...
Acknowledgments and Disclosure of Funding This work was partially supported by the German Research Foundation (DFG) within the Collaborative Research Center "On-The-Fly Computing" (SFB 901/3 project no ...
arXiv:2011.08784v1
fatcat:rj3m3nmqknhdbhfazvshcjocsa
Machine Learning for Online Algorithm Selection under Censored Feedback
[article]
2021
arXiv
pre-print
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A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
In online algorithm selection (OAS), instances of an algorithmic problem class are presented to an agent one after another, and the agent has to quickly select a presumably best algorithm from a fixed ...
In an extensive experimental evaluation on an adapted version of the ASlib benchmark, we demonstrate that theoretically well-founded methods based on Thompson sampling perform specifically strong and improve ...
Acknowledgments This work was partially supported by the German Research Foundation (DFG) within the Collaborative Research Center "On-The-Fly Computing" (SFB 901/3 project no. 160364472). ...
arXiv:2109.06234v1
fatcat:ircoyuhxdzh6tnefdzjuoqbwtm
Algorithm Selection on a Meta Level
[article]
2021
arXiv
pre-print
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The file type is application/pdf.
Although most of these methods are based on machine learning, surprisingly little work has been done on meta learning, that is, on taking advantage of the complementarity of existing algorithm selection ...
In this paper, we introduce the problem of meta algorithm selection, which essentially asks for the best way to combine a given set of algorithm selectors. ...
Acknowledgements This work was partially supported by the German Research Foundation (DFG) within the Collaborative Research Center "On-The-Fly Computing" (SFB 901/3 project no. 160364472) and the German ...
arXiv:2107.09414v1
fatcat:4dygwremnndlxjot6l5re7s7ba