Efficient Distance Approximation for Structured High-Dimensional Distributions via Learning

• URL: http://arxiv.org/abs/2002.05378v2
• Date: Fri, 14 Feb 2020 03:03:10 GMT
• Title: Efficient Distance Approximation for Structured High-Dimensional Distributions via Learning
• Authors: Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S. Meel, N. V. Vinodchandran
• Abstract summary: We design efficient distance approximation algorithms for several classes of structured high-dimensional distributions. Our results are the first efficient distance approximation algorithms for these well-studied problems.
• Score: 31.552581550603005
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 $P_1$ and $P_2$ on $n$ variables with bounded width, approximate $d_{tv}(P_1, P_2)$ to within additive error $\epsilon$ using $poly(n,\epsilon)$ samples and time - Given sample access to two $n$-dimensional Gaussians $P_1$ and $P_2$, approximate $d_{tv}(P_1, P_2)$ to within additive error $\epsilon$ using $poly(n,\epsilon)$ samples and time - Given access to observations from two causal models $P$ and $Q$ on $n$ variables that are defined over known causal graphs, approximate $d_{tv}(P_a, Q_a)$ to within additive error $\epsilon$ using $poly(n,\epsilon)$ samples, where $P_a$ and $Q_a$ are the interventional distributions obtained by the intervention $do(A=a)$ on $P$ and $Q$ respectively for a particular variable $A$. Our results are the first efficient distance approximation algorithms for these well-studied problems. They are derived using a simple and general connection to distribution learning algorithms. The distance approximation algorithms imply new efficient algorithms for {\em tolerant} testing of closeness of the above-mentioned structured high-dimensional distributions.

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