ICML 2021spotlight0 citations
Compressed Maximum Likelihood
Abstract
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.
BibTeX
@InProceedings{pmlr-v139-hao21c,
title = {Compressed Maximum Likelihood},
author = {Hao, Yi and Orlitsky, Alon},
booktitle = {Proceedings of the 38th International Conference on Machine Learning},
pages = {4085--4095},
year = {2021},
editor = {Meila, Marina and Zhang, Tong},
volume = {139},
series = {Proceedings of Machine Learning Research},
month = {18--24 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v139/hao21c/hao21c.pdf},
url = {https://proceedings.mlr.press/v139/hao21c.html},
abstract = {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.}
}