6 papers
Multi-Task Learning of Conditional Mean Operators: applications to dynamical systems and uncertainty quantification
Sami Chemlal, Thibaut Germain, Rémi Flamary +2
Estimating conditional statistics and learning representations of a population of conditional distributions are central problems in many data-driven applications, including uncerta…
Graph Matching Relaxations and Amortization for Supervised Graph Prediction
Federico Méndez, Paul Krzakala, Gabriel Melo +3
End-to-end Supervised Graph Prediction (SGP) requires a permutation-invariant loss to compare predicted and target graphs with arbitrary node orderings. Such losses typically invol…
Geometry of Relaxed Fair Regression: A Unified Framework for Aware and Unaware Settings
M. Generali Lince, Marie Generali Lince, Vincent Divol +7
Fairness-accuracy trade-offs are a central concern in the deployment of fairness-aware machine learning methods. When sensitive attributes are unavailable at inference time-the so…
Learning fMRI activations dictionaries across individual geometries via optimal transport
Sonia Mazelet, Rémi Flamary, Bertrand Thirion
Dictionary learning is a powerful tool for creating interpretable representations. When applied to functional magnetic resonance imaging (fMRI) data, the resulting patterns of brai…
MSAlign: Aligning Molecule and Mass Spectra representations for Metabolite Identification
Paul Krzakala, Gabriel Melo, Camille Lançon +4
Accurately identifying metabolites i.e. small molecules from mass spectrometry data remains a core challenge in metabolomics, with broad applications in drug discovery, environment…
Geometric Dictionary Learning of Dynamical Systems with Optimal Transport
Thibaut Germain, Sami Chemlal, Rémi Flamary +2
Learning dynamical systems through operator-theoretic representations provides a powerful framework for analyzing complex dynamics, as spectral quantities such as eigenvalues and i…