Efficient Distance Approximation for Structured High-Dimensional Distributions via Learning

摘要

We design efficient distance approximation algorithms for several classes of well-studied structured high-dimensional distributions. Specifically, we present algorithms for the following problems (where dTV is the total variation distance):