Compressed Maximum Likelihood

Maximum likelihood (ML) is one of the most fundamental and general statistical estimation techniques. Inspired by recent advances in estimating distribution functionals, we propose compressed maximum likelihood (CML) that applies ML to compressed samples. We show that CML is sample-efficient for several fundamental learning tasks over both discrete and continuous domains, including learning structural densities, estimating probability multisets, and inferring symmetric distribution functionals.