Disentangled Deep Smoothed Bootstrap for Fair Imbalanced Regression

Our paper, Disentangled Deep Smoothed Bootstrap for Fair Imbalanced Regression, with Samuel Stocksieker and Denys Pommeret has been published in Procedia Computer Science

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.

KurtHGR: A Neural Maximal Correlation for Tabular Datasets

Our paper, KurtHGR: A Neural Maximal Correlation for Tabular Datasets, with Samuel Stocksieker and Denys Pommeret has been published in Procedia Computer Science

The study of dependencies between variables is a fundamental pillar of machine learning, influencing areas as diverse as feature selection, fairness, dimensionality reduction, and multimodal learning. Among nonlinear correlation measures, the Hirschfeld-Gebelein-Rényi (HGR) maximal correlation stands out for its universality and remarkable theoretical properties. Defined as the maximum achievable correlation between nonlinear transformations of two random variables, it provides an intrinsic quantification of statistical dependence, regardless of their marginal distributions. However, despite its theoretical potential, its practical adoption still faces several challenges. In this paper, we present a new approach called KurtHGR, dedicated to the estimation of the bivariate nonlinear correlation matrix of a set of variables. We show that this solution is effective in detecting nonlinear correlations, robust to noise, and computationally efficient, thanks to a neural architecture specifically designed for this purpose. We evaluate its performance through numerical illustrations and feature selection experiments, where we demonstrate that KurtHGR empirically outperforms state-of-the-art approaches.