Robust Multi-Task Regression with Grossly Corrupted Observations
Abstract
We consider the multiple-response regression problem, where the response is subject to *sparse gross errors*, in the high-dimensional setup. We propose a tractable regularized M-estimator that is robust to such error, where the sum of two individual regularization terms are used: the first one encourages row-sparse regression parameters, and the second one encourages a sparse error term. We obtain non-asymptotical estimation error bounds of the proposed method. To the best of our knowledge, this is the first analysis of the robust multi-task regression problem with gross errors.
Cite
Text
Xu and Leng. "Robust Multi-Task Regression with Grossly Corrupted Observations." Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012.Markdown
[Xu and Leng. "Robust Multi-Task Regression with Grossly Corrupted Observations." Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012.](https://mlanthology.org/aistats/2012/xu2012aistats-robust/)BibTeX
@inproceedings{xu2012aistats-robust,
title = {{Robust Multi-Task Regression with Grossly Corrupted Observations}},
author = {Xu, Huan and Leng, Chenlei},
booktitle = {Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics},
year = {2012},
pages = {1341-1349},
volume = {22},
url = {https://mlanthology.org/aistats/2012/xu2012aistats-robust/}
}