Van Assel, Hugues

8 publications

TMLR 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

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

NeurIPS 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

NeurIPSW 2024 A Graph Matching Approach to Balanced Data Sub-Sampling for Self-Supervised Learning Hugues Van Assel, Randall Balestriero

NeurIPSW 2023 Interpolating Between Clustering and Dimensionality Reduction with Gromov-Wasserstein Hugues Van Assel, Cédric Vincent-Cuaz, Titouan Vayer, Rémi Flamary, Nicolas Courty

NeurIPSW 2023 Optimal Transport with Adaptive Regularisation Hugues Van Assel, Titouan Vayer, Rémi Flamary, Nicolas Courty

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

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