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Boosting worst-group accuracy without group annotations
Vincent Bardenhagen · Alexandru Tifrea · Fanny Yang

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in Distribution shifts: connecting methods and applications (DistShift) »
Event URL: https://openreview.net/forum?id=-D8l1ifCHYi »

Despite having good average test accuracy, classification models can have poor performance on subpopulations that are not well represented in the training set. In this work we introduce a method to improve prediction accuracy on underrepresented groups that does not require any group labels for training or validation, unlike existing approaches. We provide a sound empirical investigation of our procedure and show that it recovers the worst-group performance of methods that use oracle group annotations.

Author Information

Vincent Bardenhagen (Swiss Federal Institute of Technology)
Alexandru Tifrea (Swiss Federal Institute of Technology)
Fanny Yang (ETH Zurich)

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