# 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} }

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

Create an AI-powered research feed to stay up to date with new papers like this posted to ArXiv