NeurIPS 2019poster22 citations
Multiclass Performance Metric Elicitation
Gaurush Hiranandani, Shant Boodaghians, Ruta Mehta, Oluwasanmi O Koyejo
Abstract
Metric Elicitation is a principled framework for selecting the performance metric that best reflects implicit user preferences. However, available strategies have so far been limited to binary classification. In this paper, we propose novel strategies for eliciting multiclass classification performance metrics using only relative preference feedback. We also show that the strategies are robust to both finite sample and feedback noise.
BibTeX
@inproceedings{NEURIPS2019_1fd09c5f,
author = {Hiranandani, Gaurush and Boodaghians, Shant and Mehta, Ruta and Koyejo, Oluwasanmi O},
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 = {Multiclass Performance Metric Elicitation},
url = {https://proceedings.neurips.cc/paper_files/paper/2019/file/1fd09c5f59a8ff35d499c0ee25a1d47e-Paper.pdf},
volume = {32},
year = {2019}
}