Corpus ID: 211097048

Efficient Distance Approximation for Structured High-Dimensional Distributions via Learning

@article{Bhattacharyya2020EfficientDA,
  title={Efficient Distance Approximation for Structured High-Dimensional Distributions via Learning},
  author={Arnab Bhattacharyya and Sutanu Gayen and Kuldeep S. Meel and N. V. Vinodchandran},
  journal={ArXiv},
  year={2020},
  volume={abs/2002.05378}
}
  • Arnab Bhattacharyya, Sutanu Gayen, +1 author N. V. Vinodchandran
  • Published in ArXiv 2020
  • Mathematics, Computer Science
  • We design efficient distance approximation algorithms for several classes of structured high-dimensional distributions. Specifically, we show algorithms for the following problems: Given sample access to two Bayesian networks $P_1$ and $P_2$ over known directed acyclic graphs $G_1$ and $G_2$ having $n$ nodes and bounded in-degree, approximate $d_{tv}(P_1,P_2)$ to within additive error $\epsilon$ using $poly(n,\epsilon)$ samples and time Given sample access to two ferromagnetic Ising models… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 42 REFERENCES

    Learning Graphical Models Using Multiplicative Weights

    • Adam R. Klivans, Raghu Meka
    • Mathematics, Computer Science
    • 2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS)
    • 2017

    A Survey on Distribution Testing: Your Data is Big. But is it Blue?

    • Clément L. Canonne
    • Mathematics, Computer Science
    • Electronic Colloquium on Computational Complexity
    • 2015