Sophie's Daily Note

 Goal: improve the worst-group performance without training group annotations Methods: first identify training examples that are misclassified by a standard ERM model; and then train the final model by up-weighting the examples identified in the first stage  

Background: Neural networks fail to perform well on certain groups of the data. The group information may be expensive to obtain.  Previous work: improve worst-group performance even when group labels are unavailable for robustness and fairness Problem: improve group robustness when only some group labels are available  Methods: a two-step framework to utilize the partial labels for training data and then use the predicted group labels in a robust optimization objective Keywords:  DRO - Distributionally Robust Optimization GDRO - Group Distributionally Robust Optimization