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@COMMENT This file came from Kuldeep S. Meel's publication pages at
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@inproceedings{DFM20,
  author={
    Dudek, Jeffrey M. and
    Fried, Dror and
    Meel, Kuldeep S.
  },
  title={Taming Discrete Integration via the Boon of Dimensionality},
  booktitle=NIPS,
  month=dec,
  year={2020},
  bib2html_rescat={Counting},
  bib2html_pubtype={Refereed Conference},
  url={https://arxiv.org/abs/2010.10724},
  abstract={
    Discrete integration is a fundamental problem in computer science that
    concerns the computation of discrete sums over exponentially large sets.
    Despite intense interest from researchers for over three decades, the design
    of scalable techniques for computing estimates with rigorous guarantees for
    discrete integration remains the holy grail. The key contribution of this
    work addresses this scalability challenge via an efficient reduction of
    discrete integration to model counting. The proposed reduction is achieved
    via a significant increase in the dimensionality that, contrary to
    conventional wisdom, leads to solving an instance of the relatively simpler
    problem of model counting.
    Building on the promising approach proposed by Chakraborty et al, our work
    overcomes the key weakness of their approach: a restriction to dyadic
    weights. We augment our proposed reduction, called DeWeight, with a state of
    the art efficient approximate model counter and perform detailed empirical
    analysis over benchmarks arising from neural network verification domains,
    an emerging application area of critical importance. DeWeight, to the best
    of our knowledge, is the first technique to compute estimates with provable
    guarantees for this class of benchmarks.
  },
}