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@COMMENT This file came from Kuldeep S. Meel's publication pages at
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@inproceedings{MSV17,
  title={On Hashing-Based Approaches to Approximate DNF-Counting},
  author={Meel, Kuldeep S. and Shrotri, Aditya A. and Vardi, Moshe Y.},
  year={2017},
  booktitle=FSTTCS,
  month=dec,
  bib2html_dl_pdf={../Papers/fsttcs17.pdf},
  bib2html_pubtype={Refereed Conference},
  bib2html_rescat={Counting},
  abstract={
    Propositional model counting is a fundamental problem in artificial
    intelligence with a wide variety of applications, such as probabilistic
    inference, decision making under uncertainty, and probabilistic databases.
    Consequently, the problem is of theoretical as well as practical interest.
    When the constraints are expressed as DNF formulas, Monte Carlo-based
    techniques
    have been shown to provide a fully polynomial randomized approximation
    scheme (FPRAS). For CNF constraints, hashing-based approximation techniques
    have been demonstrated to be highly successful. Furthermore, it was shown
    that hashing-based techniques also yield an FPRAS for DNF counting without
    usage of Monte Carlo sampling. Our analysis, however, shows that the
    proposed hashing-based approach to DNF counting provides poor time
    complexity compared to the Monte Carlo-based DNF counting techniques. Given
    the success of hashing-based techniques for CNF constraints, it is natural
    to ask: Can hashing-based techniques provide an efficient FPRAS for DNF
    counting? In this paper, we provide a positive answer to this question.
    To this end, we introduce two novel algorithmic techniques: Symbolic Hashing
    and Stochastic Cell Counting, along with a new hash family of Row-Echelon
    hash functions. These innovations allow us to design a hashing-based FPRAS
    for DNF counting of similar complexity as that of prior works. Furthermore,
    we expect
    these techniques to have potential applications beyond DNF counting.
  },
}