Research map / Label-efficient and robust learning / Recognition and long-tail
Selective Classification Can Magnify Disparities Across Groups
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| apply softmax response selective classification to ERM-trained models | ERM models on CelebA · CelebA | worst-group selective accuracy | Hurt |
| apply softmax response selective classification to ERM-trained models | ERM models across five datasets · CelebA, Waterbirds, CheXpert-device, CivilComments, MultiNLI | average selective accuracy | Helped |
| apply softmax response selective classification to group DRO models | group DRO models · CelebA, Waterbirds, CheXpert-device, CivilComments, MultiNLI | worst-group selective accuracy | Helped |
| train with group DRO instead of ERM (reduces full-coverage group disparity) (ablation) | selective classification on standard models · five datasets | worst-group selective accuracy | Helped |
| switch selective confidence from softmax response to Monte Carlo dropout (ablation) | ERM selective classifiers · five datasets | worst-group selective accuracy | No clear effect |
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