Editing a classifier by rewriting its prediction rulesDownload PDF

Published: 09 Nov 2021, Last Modified: 22 Oct 2023NeurIPS 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.
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Supplementary Material: pdf
Code: https://github.com/MadryLab/EditingClassifiers
Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 5 code implementations](https://www.catalyzex.com/paper/arxiv:2112.01008/code)
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