A Probabilistic Graph Coupling View of Dimension Reduction. - Hugues Van Assel; Thibault Espinasse; Julien Chiquet; Franck Picard (NeurIPS, 2022)

SNEkhorn: Dimension Reduction with Symmetric Entropic Affinities. - Hugues Van Assel; Titouan Vayer; Rémi Flamary; Nicolas Courty (NeurIPS, 2023)

Optimal Transport with Adaptive Regularisation. - Hugues Van Assel; Titouan Vayer; Rémi Flamary; Nicolas Courty (CoRR, 2023)

Interpolating between Clustering and Dimensionality Reduction with Gromov-Wasserstein. - Hugues Van Assel; Cédric Vincent-Cuaz; Titouan Vayer; Rémi Flamary; Nicolas Courty (CoRR, 2023)

Distributional Reduction: Unifying Dimensionality Reduction and Clustering with Gromov-Wasserstein Projection. - Hugues Van Assel; Cédric Vincent-Cuaz; Nicolas Courty; Rémi Flamary; Pascal Frossard; Titouan Vayer (CoRR, 2024)

Statistical signature of subtle behavioral changes in large-scale assays. - Alexandre Blanc; François Laurent; Alex Barbier-Chebbah; Hugues Van Assel; Benjamin T. Cocanougher; Benjamin M. W. Jones; Peter Hague; Marta Zlatic; Rayan Chikhi; Christian L. Vestergaard; Tihana Jovanic; Jean-Baptiste Masson; et al. (PLoS Comput. Biol., 2025)

Distributional Reduction: Unifying Dimensionality Reduction and Clustering with Gromov-Wasserstein. - Hugues Van Assel; Cédric Vincent-Cuaz; Nicolas Courty; Rémi Flamary; Pascal Frossard; Titouan Vayer (Trans. Mach. Learn. Res., 2025)

Ditch the Denoiser: Emergence of Noise Robustness in Self-Supervised Learning from Data Curriculum. - Wenquan Lu; Jiaqi Zhang; Hugues Van Assel; Randall Balestriero (CoRR, 2025)

Joint Embedding vs Reconstruction: Provable Benefits of Latent Space Prediction for Self Supervised Learning. - Hugues Van Assel; Mark Ibrahim; Tommaso Biancalani; Aviv Regev; Randall Balestriero (CoRR, 2025)