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cs.LG2025
Improved Balanced Classification with Theoretically Grounded Loss Functions
Corinna Cortes, Mehryar Mohri, Yutao Zhong
The balanced loss is a widely adopted objective for multi-class classification under class imbalance. By assigning equal importance to all classes, regardless of their frequency, i…
cs.LG2025
Balancing the Scales: A Theoretical and Algorithmic Framework for Learning from Imbalanced Data
Corinna Cortes, Anqi Mao, Mehryar Mohri +1
Class imbalance remains a major challenge in machine learning, especially in multi-class problems with long-tailed distributions. Existing methods, such as data resampling, cost-se…