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Spotlight
Compressed Maximum Likelihood
Yi Hao · Alon Orlitsky
Maximum likelihood (ML) is one of the most fundamental and general statistical estimation techniques. Inspired by recent advances in estimating distribution functionals, we propose $\textit{compressed maximum likelihood}$ (CML) that applies ML to the compressed samples. We then show that CML is sample-efficient for several essential learning tasks over both discrete and continuous domains, including learning densities with structures, estimating probability multisets, and inferring symmetric distribution functionals.
Author Information
Yi Hao (University of California, San Diego)
Alon Orlitsky (UCSD)
Related Events (a corresponding poster, oral, or spotlight)
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2021 Poster: Compressed Maximum Likelihood »
Thu. Jul 22nd 04:00 -- 06:00 AM Room Virtual
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