Disentangled Deep Smoothed Bootstrap for Fair Imbalanced Regression

• URL: http://arxiv.org/abs/2508.13829v1
• Date: Tue, 19 Aug 2025 13:40:04 GMT
• Title: Disentangled Deep Smoothed Bootstrap for Fair Imbalanced Regression
• Authors: Samuel Stocksieker, Denys pommeret, Arthur Charpentier,
• Abstract summary: Imbalanced distribution learning is a common and significant challenge in predictive modeling, often reducing the performance of standard algorithms.<n>We propose using Variational Autoencoders (VAEs) to model and define a latent representation of data distributions.<n>To address this, we develop an innovative data generation method that combines a disentangled VAE with a Smoothed Bootstrap applied in the latent space.
• Score: 1.2289361708127877
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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