3 papers
stat.ML2026
When Does Synthetic Data Augmentation Improve Score-Based Imbalanced Classification?
Zhengchi Ma, Pengfei Lyu, Anru R. Zhang
Synthetic data augmentation is widely used to mitigate class imbalance, but its theoretical effects on score-based classification remain poorly understood. This paper develops a fr…
stat.ML2026
Synthetic Augmentation in Imbalanced Learning: When It Helps, When It Hurts, and How Much to Add
Zhengchi Ma, Anru R. Zhang
Imbalanced classification often causes standard training procedures to prioritize the majority class and perform poorly on rare but important cases. A classic and widely used remed…
stat.ML2025
Bias-Corrected Data Synthesis for Imbalanced Learning
Pengfei Lyu, Zhengchi Ma, Linjun Zhang +1
Class imbalance complicates probabilistic classification because standard training objectives emphasize majority-class performance. Synthetic oversampling can reduce imbalance, but…