Editing a classifier by rewriting its prediction rulesDownload PDF

21 May 2021, 20:47 (modified: 27 Dec 2021, 10:00)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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