Filters








4 Hits in 1.9 sec

Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis [article]

Alexander Tornede, Marcel Wever, Stefan Werner, Felix Mohr, Eyke Hüllermeier
2020 arXiv   pre-print
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]

Alexander Tornede, Marcel Wever, Eyke Hüllermeier
2020 arXiv   pre-print
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]

Alexander Tornede and Viktor Bengs and Eyke Hüllermeier
2021 arXiv   pre-print
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]

Alexander Tornede, Lukas Gehring, Tanja Tornede, Marcel Wever, Eyke Hüllermeier
2021 arXiv   pre-print
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