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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.
  },
}