@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
@COMMENT http://www.comp.nus.edu.sg/~meel/publications/
@inproceedings{BGMMPV24,
  title={Total Variation Distance Meets Probabilistic Inference},
  abstract={
    In this paper, we establish a novel connection between total variation (TV)
    distance estimation and probabilistic inference. In particular, we present
    an efficient, structure-preserving reduction from relative approximation of
    TV distance to probabilistic inference over directed graphical models. This
    reduction leads to a fully polynomial randomized approximation scheme
    (FPRAS) for estimating TV distances between same-structure distributions
    over any class of Bayes nets for which there is an efficient probabilistic
    inference algorithm. In particular, it leads to an FPRAS for estimating TV
    distances between distributions that are defined over a common Bayes net of
    small treewidth. Prior to this work, such approximation schemes only existed
    for estimating TV distances between product distributions. Our approach
    employs a new notion of partial couplings of high-dimensional distributions,
    which might be of independent interest.
  },
  author={
    Bhattacharyya, Arnab
    and Gayen, Sutanu
    and Meel, Kuldeep S.
    and Myrisiotis, Dimitrios
    and Pavan, A.
    and Vinodchandran, N. V.
  },
  year={2024},
  booktitle=ICML,
  month=jul,
  bib2html_rescat={Distribution Testing},
  bib2html_pubtype={Refereed Conference},
  bib2html_dl_pdf={https://arxiv.org/pdf/2309.09134},
}