robust-training

This paper studies symmetrization of loss functions for robust training under label noise, introducing SGCE and alpha-MAE loss functions that interpolate between multi-class unhinged loss and Mean Absolute Error, with theoretical guarantees and competitive empirical performance.

This paper studies fairness in toxicity classification across three axes: ranking, calibration, and abstention. It compares ERM, reweighted ERM, and Group DRO methods with post-hoc interventions, finding that calibration disparity is a hidden fairness violation and that abstention itself can be unfair.