@inproceedings{9115b6c49f784e5191a2228055640de0,
title = "Learning with rejection",
abstract = "We introduce a novel framework for classification with a rejection option that consists of simultaneously learning two functions: a classifier along with a rejection function. We present a full theoretical analysis of this framework including new data-dependent learning bounds in terms of the Rademacher complexities of the classifier and rejection families as well as consistency and calibration results. These theoretical guarantees guide us in designing new algorithms that can exploit different kernel-based hypothesis sets for the classifier and rejection functions. We compare and contrast our general framework with the special case of confidence-based rejection for which we devise alternative loss functions and algorithms as well. We report the results of several experiments showing that our kernel-based algorithms can yield a notable improvement over the best existing confidence-based rejection algorithm.",
author = "Corinna Cortes and Giulia DeSalvo and Mehryar Mohri",
note = "Funding Information: This work was partly funded by NSF IIS-1117591, CCF-1535987, and DGE-1342536. Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.; 27th International Conference on Algorithmic Learning Theory, ALT 2016 ; Conference date: 19-10-2016 Through 21-10-2016",
year = "2016",
doi = "10.1007/978-3-319-46379-7_5",
language = "English (US)",
isbn = "9783319463780",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "67--82",
editor = "Simon, {Hans Ulrich} and Sandra Zilles and Ronald Ortner",
booktitle = "Algorithmic Learning Theory - 27th International Conference, ALT 2016, Proceedings",
}