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@inproceedings{BGMMPV25a,
  title={Computational Explorations of Total Variation Distance},
  author={
    Bhattacharyya, Arnab and Gayen, Sutanu and Meel, Kuldeep S. and Myrisiotis,
    Dimitrios and Pavan, A. and Vinodchandran, N. V.
  },
  booktitle=ICLR,
  year={2025},
  bib2html_rescat={Distribution Testing},
  bib2html_pubtype={Refereed Conference},
  bib2html_dl_pdf={https://arxiv.org/pdf/2412.10370},
  abstract={
    We investigate some previously unexplored (or underexplored) computational
    aspects of total variation (TV) distance. First, we give a simple
    deterministic polynomial-time algorithm for checking equivalence between
    mixtures of product distributions, over arbitrary alphabets. This
    corresponds to a special case, whereby the TV distance between the two
    distributions is zero. Second, we prove that unless $\mathsf{NP} \subseteq
    \mathsf{RP}$, it is impossible to efficiently estimate the TV distance
    between arbitrary Ising models, even in a bounded-error randomized setting.
  },
}