# Bias-Corrected Data Synthesis for Imbalanced Learning

arXiv: https://arxiv.org/abs/2510.26046 · HTML: https://mlsanity.com/paper/2510.26046/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Model collapse from synthetic data, one of 13 directions in Synthetic data and self-improvement for LLMs.
- Builds on 22 of its 68 references that are in the map.
- First posted on arXiv 30 Oct 2025.

## Placed in

- Synthetic data and self-improvement for LLMs / [Model collapse from synthetic data](https://mlsanity.com/synth/model-collapse-from-synthetic-data/)

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks](https://mlsanity.com/paper/1905.11001/) (2019)
- [Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning](https://mlsanity.com/paper/1609.06570/) (2016)
- [How Does Mixup Help With Robustness and Generalization?](https://mlsanity.com/paper/2010.04819/) (2020)
- [Machine Learning for Synthetic Data Generation: A Review](https://mlsanity.com/paper/2302.04062/) (2023)
- [When and How Mixup Improves Calibration](https://mlsanity.com/paper/2102.06289/) (2021)
- [The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression](https://mlsanity.com/paper/2202.09101/) (2022)
- [Conditional Data Synthesis Augmentation](https://mlsanity.com/paper/2504.07426/) (2025)
- [FarmTest: Factor-Adjusted Robust Multiple Testing with Approximate False Discovery Control](https://mlsanity.com/paper/1711.05386/) (2017)
- [Do we need rebalancing strategies? A theoretical and empirical study around SMOTE and its variants](https://mlsanity.com/paper/2402.03819/) (2024)
- [Concentration and excess risk bounds for imbalanced classification with synthetic oversampling](https://mlsanity.com/paper/2510.20472/) (2025)
- [Revisit the Imbalance Optimization in Multi-task Learning: An Experimental Analysis](https://mlsanity.com/paper/2509.23915/) (2025)
- [A Machine Learning based Robust Prediction Model for Real-life Mobile Phone Data](https://mlsanity.com/paper/1902.07588/) (2019)
- [An Empirical Analysis of the Efficacy of Different Sampling Techniques for Imbalanced Classification](https://mlsanity.com/paper/2208.11852/) (2022)

## Built on by (most cited first, 6 of 6)

- [Synthetic Augmentation in Imbalanced Learning: When It Helps, When It Hurts, and How Much to Add](https://mlsanity.com/paper/2601.16120/) (2026)
- [Harnessing Synthetic Data from Generative AI for Statistical Inference](https://mlsanity.com/paper/2603.05396/) (2026)
- [Generative Synthetic Data for Causal Inference: Pitfalls, Remedies, and Opportunities](https://mlsanity.com/paper/2604.23904/) (2026)
- [Maximum Likelihood Estimation for Entity Ranking under Iterative Synthetic Data Augmentation](https://mlsanity.com/paper/2609.32987/) (2026)
- [When Does Synthetic Data Augmentation Improve Score-Based Imbalanced Classification?](https://mlsanity.com/paper/2606.26053/) (2026)
- [Synthetic minority data is redundant or invalid: a data-dependent validity theory and a de-biased test](https://mlsanity.com/paper/2607.20787/) (2026)

## Abstract (arXiv)

Class imbalance complicates probabilistic classification because standard training objectives emphasize majority-class performance. Synthetic oversampling can reduce imbalance, but discrepancies between the synthetic and target minority distributions may bias the fitted classifier, especially because synthetic samples depend on the observed data. We propose a bias-correction procedure that estimates the generator-induced loss discrepancy from a held-out subset of majority observations and transfers this correction to the minority class under a uniform bias-transfer condition. We establish finite-sample bounds for bias transfer and for the excess balanced risk of the resulting empirical risk minimizer, and characterize a regime in which SMOTE induces non-negligible loss bias. The framework can also be implemented in imbalanced multi-task learning and propensity-score estimation, with details provided in the Supplementary Material. Real data analyses show that the correction is most useful when synthetic distortion is appreciable.