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@inproceedings{CMV16,
	title={Algorithmic Improvements in Approximate Counting for Probabilistic Inference:
    From Linear to Logarithmic SAT Calls},
  author={Chakraborty, Supratik and Meel, Kuldeep S. and Vardi, Moshe Y.},
	code={https://bitbucket.org/kuldeepmeel/approxmc},
	year={2016},
  bib2html_dl_pdf={../Papers/ijcai16_counting.pdf},
	month=jul,
	booktitle=IJCAI,
	  bib2html_rescat={Counting},
	bib2html_pubtype={Refereed Conference},
		abstract={Probabilistic inference via model counting has emerged as a scalable
technique with strong formal guarantees, thanks to recent advances in
hashing-based approximate counting. State-of-the-art hashing-based
counting algorithms use an {\NP} oracle, such that the number of
oracle invocations grows linearly in the number of variables n in
the input constraint. We present a new approach to hashing-based
approximate model counting in which the number of oracle invocations
grows logarithmically in $n$, while still providing strong theoretical
guarantees. Our experiments show that the new approach outperforms
state-of-the-art techniques for approximate counting by 1-2 orders
of magnitude in running time.},	
}