6 papers
Outcome-Aware Spectral Feature Learning for Instrumental Variable Regression
Dimitri Meunier, Jakub Wornbard, Vladimir R. Kostic +5
We address the problem of causal effect estimation in the presence of hidden confounders using nonparametric instrumental variable (IV) regression. An established approach is to us…
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…
VertCoHiRF: Decentralized Vertical Clustering Beyond k-means
Bruno Belucci, Karim Lounici, Vladimir R. Kostic +1
Vertical Federated Learning (VFL) enables collaborative analysis across parties holding complementary feature views of the same samples, yet existing approaches are largely restric…
CoHiRF: Hierarchical Consensus for Interpretable Clustering Beyond Scalability Limits
Katia Meziani, Bruno Belucci, Karim Lounici +1
We introduce CoHiRF (Consensus Hierarchical Random Features), a hierarchical consensus framework that enables existing clustering methods to operate beyond their usual computationa…
Demystifying Spectral Feature Learning for Instrumental Variable Regression
Dimitri Meunier, Antoine Moulin, Jakub Wornbard +2
We address the problem of causal effect estimation in the presence of hidden confounders, using nonparametric instrumental variable (IV) regression. A leading strategy employs spec…
A Spectral-Grassmann Wasserstein metric for operator representations of dynamical systems
Thibaut Germain, Rémi Flamary, Vladimir R. Kostic +1
The geometry of dynamical systems estimated from trajectory data is a major challenge for machine learning applications. Koopman and transfer operators provide a linear representat…