Efficient Regret Minimization in Non-Convex Games
Abstract
We consider regret minimization in repeated games with non-convex loss functions. Minimizing the standard notion of regret is computationally intractable. Thus, we define a natural notion of regret which permits efficient optimization and generalizes offline guarantees for convergence to an approximate local optimum. We give gradient-based methods that achieve optimal regret, which in turn guarantee convergence to equilibrium in this framework.
Cite
Text
Hazan et al. "Efficient Regret Minimization in Non-Convex Games." International Conference on Machine Learning, 2017.Markdown
[Hazan et al. "Efficient Regret Minimization in Non-Convex Games." International Conference on Machine Learning, 2017.](https://mlanthology.org/icml/2017/hazan2017icml-efficient/)BibTeX
@inproceedings{hazan2017icml-efficient,
title = {{Efficient Regret Minimization in Non-Convex Games}},
author = {Hazan, Elad and Singh, Karan and Zhang, Cyril},
booktitle = {International Conference on Machine Learning},
year = {2017},
pages = {1433-1441},
volume = {70},
url = {https://mlanthology.org/icml/2017/hazan2017icml-efficient/}
}