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Theoretical Guarantees of Data Augmented Last Layer Retraining Methods
Ensuring fair predictions across many distinct subpopulations in the training data can be prohibitive for large models.
Robustness to Subpopulation Shift with Domain Label Noise via Regularized Annotation of Domains
Existing methods for last layer retraining that aim to optimize worst-group accuracy (WGA) rely heavily on well-annotated groups in the training data.
Smoothly Giving up: Robustness for Simple Models
There is a growing need for models that are interpretable and have reduced energy and computational cost (e. g., in health care analytics and federated learning).
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