Disentangled Deep Smoothed Bootstrap for Fair Imbalanced Regression
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915451422900224 |
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| author | Stocksieker, Samuel pommeret, Denys Charpentier, Arthur |
| author_facet | Stocksieker, Samuel pommeret, Denys Charpentier, Arthur |
| contents | Imbalanced distribution learning is a common and significant challenge in predictive modeling, often reducing the performance of standard algorithms. Although various approaches address this issue, most are tailored to classification problems, with a limited focus on regression. This paper introduces a novel method to improve learning on tabular data within the Imbalanced Regression (IR) framework, which is a critical problem. We propose using Variational Autoencoders (VAEs) to model and define a latent representation of data distributions. However, VAEs can be inefficient with imbalanced data like other standard approaches. To address this, we develop an innovative data generation method that combines a disentangled VAE with a Smoothed Bootstrap applied in the latent space. We evaluate the efficiency of this method through numerical comparisons with competitors on benchmark datasets for IR. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_13829 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Disentangled Deep Smoothed Bootstrap for Fair Imbalanced Regression Stocksieker, Samuel pommeret, Denys Charpentier, Arthur Machine Learning Imbalanced distribution learning is a common and significant challenge in predictive modeling, often reducing the performance of standard algorithms. Although various approaches address this issue, most are tailored to classification problems, with a limited focus on regression. This paper introduces a novel method to improve learning on tabular data within the Imbalanced Regression (IR) framework, which is a critical problem. We propose using Variational Autoencoders (VAEs) to model and define a latent representation of data distributions. However, VAEs can be inefficient with imbalanced data like other standard approaches. To address this, we develop an innovative data generation method that combines a disentangled VAE with a Smoothed Bootstrap applied in the latent space. We evaluate the efficiency of this method through numerical comparisons with competitors on benchmark datasets for IR. |
| title | Disentangled Deep Smoothed Bootstrap for Fair Imbalanced Regression |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2508.13829 |