NeurIPS 2018poster7 citations
The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning
Abstract
Consider a classification problem where we have both labeled and unlabeled data available. We show that for linear classifiers defined by convex margin-based surrogate losses that are decreasing, it is impossible to construct \emph{any} semi-supervised approach that is able to guarantee an improvement over the supervised classifier measured by this surrogate loss on the labeled and unlabeled data. For convex margin-based loss functions that also increase, we demonstrate safe improvements \emph{are} possible.
BibTeX
@inproceedings{NEURIPS2018_b6a1085a,
author = {Krijthe, Jesse and Loog, Marco},
booktitle = {Advances in Neural Information Processing Systems},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning},
url = {https://proceedings.neurips.cc/paper_files/paper/2018/file/b6a1085a27ab7bff7550f8a3bd017df8-Paper.pdf},
volume = {31},
year = {2018}
}