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cs.LG2024
ECGN: A Cluster-Aware Approach to Graph Neural Networks for Imbalanced Classification
Bishal Thapaliya, Anh Nguyen, Yao Lu +7
Classifying nodes in a graph is a common problem. The ideal classifier must adapt to any imbalances in the class distribution. It must also use information in the clustering struct…
cs.LG2024★ 1 cited
Distributionally robust self-supervised learning for tabular data
Shantanu Ghosh, Tiankang Xie, Mikhail Kuznetsov
Machine learning (ML) models trained using Empirical Risk Minimization (ERM) often exhibit systematic errors on specific subpopulations of tabular data, known as error slices. Lear…