Open Problem: Improper Learning of Mixtures of Gaussians
Abstract
We ask whether there exists an efficient unsupervised learning algorithm for mixture of Gaussians in the over-complete case (number of mixtures is larger than the dimension). The notion of learning is taken to be worst-case compression-based, to allow for improper learning.
Cite
Text
Hazan and Livni. "Open Problem: Improper Learning of Mixtures of Gaussians." Annual Conference on Computational Learning Theory, 2018.Markdown
[Hazan and Livni. "Open Problem: Improper Learning of Mixtures of Gaussians." Annual Conference on Computational Learning Theory, 2018.](https://mlanthology.org/colt/2018/hazan2018colt-open/)BibTeX
@inproceedings{hazan2018colt-open,
title = {{Open Problem: Improper Learning of Mixtures of Gaussians}},
author = {Hazan, Elad and Livni, Roi},
booktitle = {Annual Conference on Computational Learning Theory},
year = {2018},
pages = {3399-3402},
url = {https://mlanthology.org/colt/2018/hazan2018colt-open/}
}