Compressed Maximum Likelihood

Yi Hao, Alon Orlitsky
Proceedings of the 38th International Conference on Machine Learning, PMLR 139:4085-4095, 2021.

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.

Cite this Paper

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.} }
Endnote
%0 Conference Paper %T Compressed Maximum Likelihood %A Yi Hao %A Alon Orlitsky %B Proceedings of the 38th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2021 %E Marina Meila %E Tong Zhang %F pmlr-v139-hao21c %I PMLR %P 4085--4095 %U https://proceedings.mlr.press/v139/hao21c.html %V 139 %X 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.
APA
Hao, Y. & Orlitsky, A.. (2021). Compressed Maximum Likelihood. Proceedings of the 38th International Conference on Machine Learning, in Proceedings of Machine Learning Research 139:4085-4095 Available from https://proceedings.mlr.press/v139/hao21c.html.

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