Convex Repeated Games and Fenchel Duality
Abstract
We describe an algorithmic framework for an abstract game which we term a convex repeated game. We show that various online learning and boosting algorithms can be all derived as special cases of our algorithmic framework. This unified view explains the properties of existing algorithms and also enables us to derive several new interesting algorithms. Our algorithmic framework stems from a connection that we build between the notions of regret in game theory and weak duality in convex optimization.
Cite
Text
Shalev-shwartz and Singer. "Convex Repeated Games and Fenchel Duality." Neural Information Processing Systems, 2006.Markdown
[Shalev-shwartz and Singer. "Convex Repeated Games and Fenchel Duality." Neural Information Processing Systems, 2006.](https://mlanthology.org/neurips/2006/shalevshwartz2006neurips-convex/)BibTeX
@inproceedings{shalevshwartz2006neurips-convex,
title = {{Convex Repeated Games and Fenchel Duality}},
author = {Shalev-shwartz, Shai and Singer, Yoram},
booktitle = {Neural Information Processing Systems},
year = {2006},
pages = {1265-1272},
url = {https://mlanthology.org/neurips/2006/shalevshwartz2006neurips-convex/}
}