Boosting Worst-Group Accuracy Without Group Annotations

Abstract

Despite having good average test accuracy, classification models can have poor performance on subpopulations that are not well represented in the training data. In this work, we introduce a criterion to estimate the accuracy on these populations. This allows us to design a procedure that achieves good worst-group performance and unlike previous procedures requires no group labels. 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.

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Cite

Text

Bardenhagen et al. "Boosting Worst-Group Accuracy Without Group Annotations." NeurIPS 2021 Workshops: DistShift, 2021.

Markdown

[Bardenhagen et al. "Boosting Worst-Group Accuracy Without Group Annotations." NeurIPS 2021 Workshops: DistShift, 2021.](https://mlanthology.org/neuripsw/2021/bardenhagen2021neuripsw-boosting/)

BibTeX

@inproceedings{bardenhagen2021neuripsw-boosting,
  title     = {{Boosting Worst-Group Accuracy Without Group Annotations}},
  author    = {Bardenhagen, Vincent and Tifrea, Alexandru and Yang, Fanny},
  booktitle = {NeurIPS 2021 Workshops: DistShift},
  year      = {2021},
  url       = {https://mlanthology.org/neuripsw/2021/bardenhagen2021neuripsw-boosting/}
}