Corpus ID: 209436501

A General Framework for Symmetric Property Estimation

@inproceedings{Charikar2019AGF,
  title={A General Framework for Symmetric Property Estimation},
  author={Moses Charikar and Kirankumar Shiragur and Aaron Sidford},
  booktitle={NeurIPS},
  year={2019}
}
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 {\em easy} region where empirical estimation works and the {\em 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… Expand

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