When Does Synthetic Data Augmentation Improve Score-Based Imbalanced Classification?
Zhengchi Ma, Pengfei Lyu, Anru R. Zhang
stat.ML, cs.LG
Submitted: 2026-06-24
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Concentration and excess risk bounds for imbalanced classification with synthetic oversampling
- GLM Inference with AI-Generated Synthetic Data Using Misspecified Linear Regression
- Auto-Encoding Variational Bayes
- Bias-Corrected Data Synthesis for Imbalanced Learning
- Synthetic Augmentation in Imbalanced Learning: When It Helps, When It Hurts, and How Much to Add
- Synthetic Oversampling: Theory and A Practical Approach Using LLMs to Address Data Imbalance
- Conditional Data Synthesis Augmentation
- Classification Imbalance as Transfer Learning
- Generative Synthetic Data for Causal Inference: Pitfalls, Remedies, and Opportunities
- Imbalance-aware Presence-only Loss Function for Species Distribution Modeling
- mixup: Beyond Empirical Risk Minimization
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