Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity
Abstract
The versatility of exponential families, along with their attendant convexity properties, make them a popular and effective statistical model. A central issue is learning these models in high-dimensions when the optimal parameter vector is sparse. This work characterizes a certain strong convexity property of general exponential families, which allows their generalization ability to be quantified. In particular, we show how this property can be used to analyze generic exponential families under L1 regularization.
Cite
Text
Kakade et al. "Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity." Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010.Markdown
[Kakade et al. "Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity." Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010.](https://mlanthology.org/aistats/2010/kakade2010aistats-learning/)BibTeX
@inproceedings{kakade2010aistats-learning,
title = {{Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity}},
author = {Kakade, Sham and Shamir, Ohad and Sindharan, Karthik and Tewari, Ambuj},
booktitle = {Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics},
year = {2010},
pages = {381-388},
volume = {9},
url = {https://mlanthology.org/aistats/2010/kakade2010aistats-learning/}
}