`

Timezone: »

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

More from the Same Authors