Feature Selection for Unsupervised Domain Adaptation Using Optimal Transport. - Léo Gautheron; Ievgen Redko; Carole Lartizien (ECML/PKDD (2), 2018)

Metric Learning from Imbalanced Data. - Léo Gautheron; Amaury Habrard; Emilie Morvant; Marc Sebban (ICTAI, 2019)

Learning Landmark-Based Ensembles with Random Fourier Features and Gradient Boosting. - Léo Gautheron; Pascal Germain; Amaury Habrard; Emilie Morvant; Marc Sebban; Valentina Zantedeschi (CoRR, 2019)

Landmark-Based Ensemble Learning with Random Fourier Features and Gradient Boosting. - Léo Gautheron; Pascal Germain; Amaury Habrard; Guillaume Metzler; Emilie Morvant; Marc Sebban; Valentina Zantedeschi (ECML/PKDD (3), 2020)

Metric Learning from Imbalanced Data with Generalization Guarantees. - Léo Gautheron; Amaury Habrard; Emilie Morvant; Marc Sebban (Pattern Recognit. Lett., 2020)

Construction de Représentation de Données Adaptées dans le Cadre de Peu d'Exemples Étiquetés. (Learning Tailored Data Representations from Few Labeled Examples). - Léo Gautheron (2020)

POT: Python Optimal Transport. - Rémi Flamary; Nicolas Courty; Alexandre Gramfort; Mokhtar Z. Alaya; Aurélie Boisbunon; Stanislas Chambon; Laetitia Chapel; Adrien Corenflos; Kilian Fatras; Nemo Fournier; Léo Gautheron; Nathalie T. H. Gayraud; et al. (J. Mach. Learn. Res., 2021)

MetaAP: A meta-tree-based ranking algorithm optimizing the average precision from imbalanced data. - Rémi Viola; Léo Gautheron; Amaury Habrard; Marc Sebban (Pattern Recognit. Lett., 2022)