Learning with rejection

Corinna Cortes, Giulia DeSalvo, Mehryar Mohri

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publicationAlgorithmic Learning Theory - 27th International Conference, ALT 2016, Proceedings
EditorsHans Ulrich Simon, Sandra Zilles, Ronald Ortner
PublisherSpringer Verlag
Number of pages16
ISBN (Print)9783319463780
StatePublished - 2016
Event27th International Conference on Algorithmic Learning Theory, ALT 2016 - Bari, Italy
Duration: Oct 19 2016Oct 21 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9925 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other27th International Conference on Algorithmic Learning Theory, ALT 2016

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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