Online Submodular Minimization
Abstract
We consider an online decision problem over a discrete space in which the loss function is submodular. We give algorithms which are computationally efficient and are Hannan-consistent in both the full information and partial feedback settings.
Cite
Text
Hazan and Kale. "Online Submodular Minimization." Journal of Machine Learning Research, 2012.Markdown
[Hazan and Kale. "Online Submodular Minimization." Journal of Machine Learning Research, 2012.](https://mlanthology.org/jmlr/2012/hazan2012jmlr-online/)BibTeX
@article{hazan2012jmlr-online,
title = {{Online Submodular Minimization}},
author = {Hazan, Elad and Kale, Satyen},
journal = {Journal of Machine Learning Research},
year = {2012},
pages = {2903-2922},
volume = {13},
url = {https://mlanthology.org/jmlr/2012/hazan2012jmlr-online/}
}