Tag
XGBoost has been updated to support multi-class and multi-label problems without the need to train separate models for each class or label, introducing the Vector-Leaf Model.
This paper proposes AHEAD, a cross-annotator learning framework for multi-class label aggregation that uses graph neural networks to model annotator reliability, achieving significant accuracy improvements on 10 real-world datasets spanning NLP, CV, Video, and Audio.
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.