BARACK: partially supervised group robustness with guarantees

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

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