NeurIPS 2019poster6 citations
The Parameterized Complexity of Cascading Portfolio Scheduling
Eduard Eiben, Robert Ganian, Iyad Kanj, Stefan Szeider
Abstract
Cascading portfolio scheduling is a static algorithm selection strategy which uses a sample of test instances to compute an optimal ordering (a cascading schedule) of a portfolio of available algorithms. The algorithms are then applied to each future instance according to this cascading schedule, until some algorithm in the schedule succeeds. Cascading algorithm scheduling has proven to be effective in several applications, including QBF solving and the generation of ImageNet classification models.
BibTeX
@inproceedings{NEURIPS2019_8ce1a43f,
author = {Eiben, Eduard and Ganian, Robert and Kanj, Iyad and Szeider, Stefan},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {The Parameterized Complexity of Cascading Portfolio Scheduling},
url = {https://proceedings.neurips.cc/paper_files/paper/2019/file/8ce1a43fb75e779c6b794ba4d255cf6d-Paper.pdf},
volume = {32},
year = {2019}
}