Theoretical Guarantees of Data Augmented Last Layer Retraining Methods
This work addresses fairness in machine learning for large models, but it is incremental as it builds on existing retraining and augmentation techniques with theoretical analysis.
The paper tackles the problem of ensuring fair predictions across subpopulations by analyzing linear last layer retraining with data augmentation methods like upweighting and downsampling, deriving optimal worst-group accuracy under Gaussian assumptions and validating results on synthetic and public datasets.
Ensuring fair predictions across many distinct subpopulations in the training data can be prohibitive for large models. Recently, simple linear last layer retraining strategies, in combination with data augmentation methods such as upweighting, downsampling and mixup, have been shown to achieve state-of-the-art performance for worst-group accuracy, which quantifies accuracy for the least prevalent subpopulation. For linear last layer retraining and the abovementioned augmentations, we present the optimal worst-group accuracy when modeling the distribution of the latent representations (input to the last layer) as Gaussian for each subpopulation. We evaluate and verify our results for both synthetic and large publicly available datasets.