NeurIPS 2019poster8 citations
Globally Optimal Learning for Structured Elliptical Losses
Yoav Wald, Nofar Noy, Gal Elidan, Ami Wiesel
Abstract
Heavy tailed and contaminated data are common in various applications of machine learning. A standard technique to handle regression tasks that involve such data, is to use robust losses, e.g., the popular Huber’s loss.
BibTeX
@inproceedings{NEURIPS2019_1cd035a3,
author = {Wald, Yoav and Noy, Nofar and Elidan, Gal and Wiesel, Ami},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Globally Optimal Learning for Structured Elliptical Losses},
url = {https://proceedings.neurips.cc/paper_files/paper/2019/file/1cd035a313edec52ac8f69c27aba683f-Paper.pdf},
volume = {32},
year = {2019}
}