@inproceedings{MaloneKJKM:OASC2017,
title =	{{AS-ASL}: Algorithm Selection with Auto-sklearn},
author = {Brandon Malone and Kustaa Kangas and Matti J\"arvisalo
          and Mikko Koivisto and Petri Myllym\"aki},
booktitle = {Proceedings of the Open Algorithm Selection Challenge 2017},
pages = {19--22},
year = {2017},
editor = {Marius Lindauer and Jan N. van Rijn and Lars Kotthoff},
volume = {79},
series = {Proceedings of Machine Learning Research},
publisher = {PMLR},
}

Abstract:

In this paper, we describe our algorithm selection with Auto-sklearn
(as-asl) software as it was entered in the 2017 Open Algorithm Selection
Challenge. as-asl first selects informative sets of features and then
uses those to predict distributions of algorithm runtimes. A classifier
uses those predictions, as well as the informative features, to select
an algorithm for each problem instance. Our source code is publicly
available with the permissive MIT license.
			    }