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Denys Pommeret

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3 papers

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NeurIPS Conference 2025 Conference Paper

SHGR: A Generalized Maximal Correlation Coefficient

Samuel Stocksieker
Denys Pommeret

Traditional correlation measures, such as Pearson’s and Spearman’s coefficients, are limited in their ability to capture complex relationships, particularly nonlinear and multivariate dependencies. The Hirschfeld–Gebelein–Rényi (HGR) maximal correlation offers a powerful alternative by measuring the highest Pearson correlation achievable through nonlinear transformations of two random variables. However, estimating the HGR coefficient remains challenging due to the complexity of optimizing arbitrary nonlinear functions. We introduce a new coefficient, satisfying Rényi's axioms, based on the extension of HGR with Spearman's rank correlation: the Spearman HGR (SHGR). We propose a neural network-based estimator tailored to estimate (i) the bivariate correlation matrix, (ii) the multivariate correlations between a set of variables and another one, and (iii) the full correlation between two sets of variables. This estimate effectively detects nonlinear dependencies and demonstrates robustness to noise, outliers, and spurious correlations (hallucinations). Additionally, it achieves competitive computational efficiency through designed neural architectures. Comprehensive numerical experiments and feature selection tasks confirm that SHGR outperforms existing state-of-the-art methods.

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JMLR Journal 2024 Journal Article

Contamination-source based K-sample clustering

Xavier Milhaud
Denys Pommeret
Yahia Salhi
Pierre Vandekerkhove

In this work, we investigate the $K$-sample clustering of populations subject to contamination phenomena. A contamination model is a two-component mixture model where one component is known (standard behaviour) and the second component, modeling a departure from the standard behaviour, is unknown.When $K$ populations from such a model are observed we propose a semiparametric clustering methodology to detect which populations are impacted by the same type of contamination, with the aim of faciliting coordinated diagnosis and best practices sharing. We prove the consistency of our approach under the assumption of the existence of true clusters and demonstrate the performances of our methodology through an extensive Monte Carlo study. Finally, we apply our methodology, implemented in the R admix package, to a European countries COVID-19 excess of mortality dataset, aiming to cluster countries similarly impacted by the pandemic across different age groups. [abs] [ pdf ][ bib ] &copy JMLR 2024. ( edit, beta )

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TMLR Journal 2024 Journal Article

Generalized Oversampling for Learning from Imbalanced datasets and Associated Theory: Application in Regression

Samuel Stocksieker
Denys Pommeret
Arthur Charpentier

In supervised learning, it is quite frequent to be confronted with real imbalanced datasets. This situation leads to a learning difficulty for standard algorithms. Research and solutions in imbalanced learning have mainly focused on classification tasks. Despite its importance, very few solutions exist for imbalanced regression. In this paper, we propose a data augmentation procedure, the GOLIATH algorithm, based on kernel density estimates and especially dedicated to the problem of imbalanced data. This general approach encompasses two large families of synthetic oversampling: those based on perturbations, such as Gaussian Noise, and those based on interpolations, such as SMOTE. It also provides an explicit form of such machine learning algorithms. New synthetic data generators are deduced. We apply GOLIATH in imbalanced regression combining such generator procedures with a new wild-bootstrap resampling technique for the target values. We evaluate the performance of the GOLIATH algorithm in imbalanced regression where we compare our approach with state-of-the-art techniques.

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