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Algorithm Configuration in the Cloud: A Feasibility Study

  • Daniel Geschwender
  • Frank Hutter
  • Lars Kotthoff
  • Yuri Malitsky
  • Holger H. Hoos
  • Kevin Leyton-Brown
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8426)

Abstract

Configuring algorithms automatically to achieve high performance is becoming increasingly relevant and important in many areas of academia and industry. Algorithm configuration methods take a parameterized target algorithm, a performance metric and a set of example data, and aim to find a parameter configuration that performs as well as possible on a given data set.

Notes

Acknowledgements

The authors were supported by an Amazon Web Services research grant, European Union FP7 grant 284715 (ICON), a DFG Emmy Noether Grant, and Compute Canada.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Daniel Geschwender
    • 1
  • Frank Hutter
    • 2
  • Lars Kotthoff
    • 3
  • Yuri Malitsky
    • 3
  • Holger H. Hoos
    • 4
  • Kevin Leyton-Brown
    • 4
  1. 1.University of Nebraska-LincolnLincolnUSA
  2. 2.University of FreiburgFreiburg im BreisgauGermany
  3. 3.INSIGHT Centre for Data AnalyticsCorkIreland
  4. 4.University of British ColumbiaVancouverCanada

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