Tag Archives: Osaka

GCKE 2025, Osaka, Annual Global Congress of Knowledge Economy

This week, I will attend the 10th Annual Global Congress of Knowledge Economy, in Osaka (大阪). On Wednesday morning, I will chair the GCKE04 session, Economic Governance & Sustainable Development. I will also give a talk on “Fairness and Insurance: Disentangling Illegitimate and Indirect Discriminations” (slides are available). It is based on recent work with Oliver and Marie-Pier Côté.

Disentangled Deep Smoothed Bootstrap for Fair Imbalanced Regression

Our paper, “Disentangled Deep Smoothed Bootstrap for Fair Imbalanced Regression” is now available on ArXiv

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.