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cs.LG2024
Label Noise Robustness for Domain-Agnostic Fair Corrections via Nearest Neighbors Label Spreading
Nathan Stromberg, Rohan Ayyagari, Sanmi Koyejo +2
Last-layer retraining methods have emerged as an efficient framework for correcting existing base models. Within this framework, several methods have been proposed to deal with cor…
cs.LG2024
Theoretical Guarantees of Data Augmented Last Layer Retraining Methods
Monica Welfert, Nathan Stromberg, Lalitha Sankar
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…
cs.LG2024
Robustness to Subpopulation Shift with Domain Label Noise via Regularized Annotation of Domains
Nathan Stromberg, Rohan Ayyagari, Monica Welfert +3
Existing methods for last layer retraining that aim to optimize worst-group accuracy (WGA) rely heavily on well-annotated groups in the training data. We show, both in theory and p…