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October 11, 2025Open Access

Disentangled Deep Smoothed Bootstrap for Fair Imbalanced Regression

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This novel approach enhances learning in imbalanced regression using variational autoencoders and bootstrap techniques.

The proposed method significantly improves predictive performance on imbalanced regression tasks, addressing a common challenge in modeling.
Using a novel combination of disentangled variational autoencoders and smoothed bootstrap, the approach generates improved latent representations of imbalanced data.
Numerical comparisons demonstrate the method's superior efficiency over traditional approaches on benchmark datasets focused on imbalanced regression.
The findings highlight the importance of tailored methods for imbalanced regression problems, suggesting broader applications in predictive modeling.

Stocksieker et al. (2025) studied this question.

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