@COMMENT This file was generated by bib2html.pl <https://sourceforge.net/projects/bib2html/> version 0.94
@COMMENT written by Patrick Riley <http://sourceforge.net/users/patstg/>
@COMMENT This file came from Kuldeep S. Meel's publication pages at
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@inproceedings{CGSM20,
author={Ciampiconi, Lorenzo and Ghosh, Bishwamittra and Scarlett, Jonathan and  Meel, Kuldeep S.},
title={A {MaxSAT}-based Framework for Group Testing},
booktitle=AAAI,
month=feb,
year={2020},
bib2html_pubtype={Refereed Conference},
bib2html_dl_pdf={../Papers/aaai20-cgsm.pdf},
code={https://github.com/meelgroup/mgt},
bib2html_rescat={Formal Methods 4 ML},	
abstract={The success of MaxSAT (maximum satisfiability) solving in recent years has motivated researchers to apply
 MaxSAT solvers in diverse discrete combinatorial optimization problems. Group testing has been studied as a combinatorial 
optimization problem, where the goal is to find defective items among a set of items by performing sets of tests on items.
 In this paper, we propose a MaxSAT-based framework, called MGT, that solves group testing, in particular, the decoding 
phase of non-adaptive group testing. We extend this approach to the noisy variant of group testing, and propose a compact
 MaxSAT-based encoding that guarantees an optimal solution. Our extensive experimental results show that MGT can solve group
 testing instances of 10000 items with 3% defectivity, which no prior work can handle to the best of our knowledge. Furthermore,
 MGT has better accuracy than the LP-based ap- proach. We also discover an interesting phase transition behavior in the runtime,
 which reveals the easy-hard-easy nature of group testing.},
}