Can Entropic Regularization Be Replaced by Squared Euclidean Distance Plus Additional Linear Constraints
Abstract
There are two main families of on-line algorithms depending on whether a relative entropy or a squared Euclidean distance is used as a regularizer. The difference between the two families can be dramatic. The question is whether one can always achieve comparable performance by replacing the relative entropy regularization by the squared Euclidean distance plus additional linear constraints. We formulate a simple open problem along these lines for the case of learning disjunctions.
Cite
Text
Warmuth. "Can Entropic Regularization Be Replaced by Squared Euclidean Distance Plus Additional Linear Constraints." Annual Conference on Computational Learning Theory, 2006. doi:10.1007/11776420_48Markdown
[Warmuth. "Can Entropic Regularization Be Replaced by Squared Euclidean Distance Plus Additional Linear Constraints." Annual Conference on Computational Learning Theory, 2006.](https://mlanthology.org/colt/2006/warmuth2006colt-entropic/) doi:10.1007/11776420_48BibTeX
@inproceedings{warmuth2006colt-entropic,
title = {{Can Entropic Regularization Be Replaced by Squared Euclidean Distance Plus Additional Linear Constraints}},
author = {Warmuth, Manfred K.},
booktitle = {Annual Conference on Computational Learning Theory},
year = {2006},
pages = {653-654},
doi = {10.1007/11776420_48},
url = {https://mlanthology.org/colt/2006/warmuth2006colt-entropic/}
}