A General Framework for Symmetric Property Estimation
Abstract
In this paper we provide a general framework for estimating symmetric properties of distributions from i.i.d. samples. For a broad class of symmetric properties we identify the easy region where empirical estimation works and the difficult region where more complex estimators are required. We show that by approximately computing the profile maximum likelihood (PML) distribution \cite{ADOS16} in this difficult region we obtain a symmetric property estimation framework that is sample complexity optimal for many properties in a broader parameter regime than previous universal estimation approaches based on PML. The resulting algorithms based on these pseudo PML distributions are also more practical.
Cite
@article{arxiv.2003.00844,
title = {A General Framework for Symmetric Property Estimation},
author = {Moses Charikar and Kirankumar Shiragur and Aaron Sidford},
journal= {arXiv preprint arXiv:2003.00844},
year = {2020}
}
Comments
Published in Neural Information Processing Systems 2019