Editing a classifier by rewriting its prediction rulesDownload PDF

May 21, 2021 (edited Oct 27, 2021)NeurIPS 2021 PosterReaders: Everyone
  • Keywords: model debugging, spurious correlations, robustness
  • TL;DR: We propose a method that allows users to rewrite high-level predictions rules with virtually no additional data collection.
  • Abstract: We propose a methodology for modifying the behavior of a classifier by directly rewriting its prediction rules. Our method requires virtually no additional data collection and can be applied to a variety of settings, including adapting a model to new environments, and modifying it to ignore spurious features.
  • Supplementary Material: pdf
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  • Code: https://github.com/MadryLab/EditingClassifiers
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