Iterative Regularization for Convex Regularizers
Abstract
We study iterative regularization for linear models, when the bias is convex but not necessarily strongly convex. We characterize the stability properties of a primal-dual gradient based approach, analyzing its convergence in the presence of worst case deterministic noise. As a main example, we specialize and illustrate the results for the problem of robust sparse recovery. Key to our analysis is a combination of ideas from regularization theory and optimization in the presence of errors. Theoretical results are complemented by experiments showing that state-of-the-art performances are achieved with considerable computational speed-ups.
Cite
Text
Molinari et al. "Iterative Regularization for Convex Regularizers." Artificial Intelligence and Statistics, 2021.Markdown
[Molinari et al. "Iterative Regularization for Convex Regularizers." Artificial Intelligence and Statistics, 2021.](https://mlanthology.org/aistats/2021/molinari2021aistats-iterative/)BibTeX
@inproceedings{molinari2021aistats-iterative,
title = {{Iterative Regularization for Convex Regularizers}},
author = {Molinari, Cesare and Massias, Mathurin and Rosasco, Lorenzo and Villa, Silvia},
booktitle = {Artificial Intelligence and Statistics},
year = {2021},
pages = {1684-1692},
volume = {130},
url = {https://mlanthology.org/aistats/2021/molinari2021aistats-iterative/}
}