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Compressed Maximum Likelihood
Yi Hao · Alon Orlitsky

Wed Jul 21 09:00 PM -- 11:00 PM (PDT) @ Virtual
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)

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