Publications

Publications principales

• SHGR: A Generalized Maximal Correlation Coefficient
  S. Stocksieker, D. Pommeret
  In Conference on Neural Information Processing Systems (Neurips), 2025

• A Comprehensive Survey on Imbalanced Regression: Definitions, Solutions, and Future Directions
  S. Stocksieker, D. Pommeret

• KurtHGR: A Neural Maximal Correlation for Tabular Datasets
  Stocksieker, S., Pommeret, D., & Charpentier, A.
  In International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES), 2025

• Disentangled Deep Smoothed Bootstrap for Fair Imbalanced Regression
  Stocksieker, S., Pommeret, D., & Charpentier, A.
  In International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES), 2025

• Delving into Deep Smoothed Bootstrap: Application in Imbalanced Regression
  Stocksieker, S., Pommeret, D., & Charpentier, A.
  In Special Track on Emerging Data Science Advances, International Conference on Data Science and Advanced Analytics (DSAA), 2025

• Data Augmentation with Variational Autoencoder for Imbalanced Dataset
  Stocksieker, S., Pommeret, D., & Charpentier, A.
  In International Conference on Neural Information Processing (ICONIP), (pp. 354-370), Springer Nature Singapore, 2024

• Generalized Oversampling for Learning from Imbalanced Datasets and Associated Theory: Application in Regression
  Stocksieker, S., Pommeret, D., & Charpentier, A.
  In Transactions on Machine Learning Research (TMLR), 2024

• Boarding for ISS: Imbalanced Self-Supervised – Discovery of a Scaled Autoencoder for Mixed Tabular Datasets
  Stocksieker, S., Pommeret, D., & Charpentier, A.
  In International Joint Conference on Neural Networks (IJCNN), pp. 1-10, IEEE, 2024

• Data Augmentation for Imbalanced Regression
  Stocksieker, S., Pommeret, D., & Charpentier, A.
  In International Conference on Artificial Intelligence and Statistics (AISTATS), pp. 7774–7799, PMLR, 2023
  Version publiée:

Publications connexes (collaborations)

• MIAMI : MIxed Data Augmentation MIxture
  R. Fuchs, D. Pommeret, S. Stocksieker
  In International Conference on Computational Science and Its Applications (ICCSA), pp. 113–129, Springer, 2022.

• MI2AMI : Missing Data Imputation Using Mixed Deep Gaussian Mixture Models
  R. Fuchs, D. Pommeret, S. Stocksieker
  In International Conference on Machine Learning, Optimization, and Data Science (LOD), pp. 211–222, Springer, 2022.

Reviewer

• TMLR: 2024, 2025, 2026
• IJCNN: 2025, 2026