Unsupervised Multi-source Domain Adaptation for Regression. - Guillaume Richard; Antoine de Mathelin; Georges Hébrail; Mathilde Mougeot; Nicolas Vayatis (ECML/PKDD (1), 2020)

Adversarial Weighting for Domain Adaptation in Regression. - Antoine de Mathelin; Guillaume Richard; Mathilde Mougeot; Nicolas Vayatis (CoRR, 2020)

Adversarial Weighting for Domain Adaptation in Regression. - Antoine de Mathelin; Guillaume Richard; François Deheeger; Mathilde Mougeot; Nicolas Vayatis (ICTAI, 2021)

Discrepancy-Based Active Learning for Domain Adaptation. - Antoine de Mathelin; Mathilde Mougeot; Nicolas Vayatis (CoRR, 2021)

ADAPT : Awesome Domain Adaptation Python Toolbox. - Antoine de Mathelin; François Deheeger; Guillaume Richard; Mathilde Mougeot; Nicolas Vayatis (CoRR, 2021)

A Binded VAE for Inorganic Material Generation. - Fouad Oubari; Antoine de Mathelin; Rodrigue Décatoire; Mathilde Mougeot (CoRR, 2021)

Unsupervised Domain Adaptation for Constraining Star Formation Histories. - Sankalp Gilda; Antoine de Mathelin; Sabine Bellstedt; Guillaume Richard (CoRR, 2021)

Discrepancy-Based Active Learning for Domain Adaptation. - Antoine de Mathelin; François Deheeger; Mathilde Mougeot; Nicolas Vayatis (ICLR, 2022)

Fast and Accurate Importance Weighting for Correcting Sample Bias. - Antoine de Mathelin; François Deheeger; Mathilde Mougeot; Nicolas Vayatis (ECML/PKDD (1), 2022)

Personalized One-Shot Collaborative Learning. - Marie Garin; Antoine de Mathelin; Mathilde Mougeot; Nicolas Vayatis (ICTAI, 2023)

Deep Anti-Regularized Ensembles provide reliable out-of-distribution uncertainty quantification. - Antoine de Mathelin; François Deheeger; Mathilde Mougeot; Nicolas Vayatis (CoRR, 2023)

Maximum Weight Entropy. - Antoine de Mathelin; François Deheeger; Mathilde Mougeot; Nicolas Vayatis (CoRR, 2023)

SKADA-Bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation. - Yanis Lalou; Théo Gnassounou; Antoine Collas; Antoine de Mathelin; Oleksii Kachaiev; Ambroise Odonnat; Alexandre Gramfort; Thomas Moreau 001; Rémi Flamary (CoRR, 2024)

Towards reliable machine learning under domain shift and costly labeling, with applications to engineering design. (Vers un apprentissage automatique fiable sous contrainte de changement de domaine et d'étiquetage coûteux. : Application à la conception produits). - Antoine de Mathelin (2024)

OneBatchPAM: A Fast and Frugal K-Medoids Algorithm. - Antoine de Mathelin; Nicolas Enrique Cecchi; François Deheeger; Mathilde Mougeot; Nicolas Vayatis (AAAI, 2025)

SKADA-Bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation On Diverse Modalities. - Yanis Lalou; Théo Gnassounou; Antoine Collas; Antoine de Mathelin; Ambroise Odonnat; Thomas Moreau 001; Alexandre Gramfort; Rémi Flamary (Trans. Mach. Learn. Res., 2025)