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@inproceedings{PM22,
  title={A scalable tester for samplers},
  author={Pote, Yash and Meel, Kuldeep S.},
  nameorder={random},
  bib2html_pubtype={Refereed Conference},
  booktitle=NIPS,
  month=nov,
  bib2html_dl_pdf={../Papers/neurips22.pdf},
  bib2html_rescat={Sampling,Distribution Testing},
  year={2022},
  abstract={
    In this paper we study the problem of testing of constrained samplers over
    high-dimensional distributions with (epsilon,eta,delta) guarantees. Samplers
    are increasingly used in a wide range of safety-critical ML applications,
    and hence the testing problem has gained importance. For n-dimensional
    distributions, the existing state-of-the-art algorithm, Barbarik2, has a
    worst case query complexity of exponential in n and hence is not ideal for
    use in practice. Our primary contribution is an exponentially faster
    algorithm that has a query complexity linear in n and hence can easily scale
    to larger instances. We demonstrate our claim by implementing our algorithm
    and then comparing it against Barbarik2. Our experiments on the samplers
    wUniGen3 and wSTS, find that Pacoco requires 10X fewer samples for wUniGen3
    and 450X fewer samples for wSTS as compared to Barbarik2.
  },
}