Fall 2018

Efficient Profile Maximum Likelihood for Universal Symmetric Property Estimation

Friday, Nov. 2, 2018 11:30 am12:10 pm PDT

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Moses Charikar (Stanford University)

Symmetric properties of distributions arise in multiple settings. For each of these, separate estimators and analysis techniques have been developed. Recently, Orlitsky et al showed that a single estimator that maximizes profile maximum likelihood (PML) is sample competitive for all symmetric properties. Further, they showed that even a 2^{n^{1-delta}}-approximate maximizer of the PML objective can serve as such a universal plug-in estimator. (Here n is the size of the sample). Unfortunately, no polynomial time computable PML estimator with such an approximation guarantee was known. We provide the first such estimator and show how to compute it in time nearly linear in n. We also present some preliminary experimental results. Joint work with Kiran Shiragur and Aaron Sidford.