Online Robust Regression via SGD on the l1 loss

Scott Pesme; Nicolas Flammarion

NeurIPS 2020poster41 citations

Online Robust Regression via SGD on the l1 loss

Abstract

We consider the robust linear regression problem in the online setting where we have access to the data in a streaming manner, one data point after the other. More specifically, for a true parameter $ \theta^* $, we consider the corrupted Gaussian linear model $y =

BibTeX

@inproceedings{NEURIPS2020_1ae6464c,
 author = {Pesme, Scott and Flammarion, Nicolas},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
 pages = {2540--2552},
 publisher = {Curran Associates, Inc.},
 title = {Online Robust Regression via SGD on the l1 loss},
 url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/1ae6464c6b5d51b363d7d96f97132c75-Paper.pdf},
 volume = {33},
 year = {2020}
}