Antoine de Mathelin

According to our database, Antoine de Mathelin authored at least 19 papers between 2020 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

Empirically Calibrated Conditional Independence Tests.

Milleno Pan, Antoine de Mathelin, Wesley Tansey

CoRR, February, 2026

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, Alexandre Gramfort, Rémi Flamary

Trans. Mach. Learn. Res., 2025

Deep Out-of-Distribution Uncertainty Quantification via Weight Entropy Maximization.

Antoine de Mathelin, François Deheeger, Mathilde Mougeot, Nicolas Vayatis

J. Mach. Learn. Res., 2025

Addressing the Cold-Start Problem for Personalized Combination Drug Screening.

Antoine de Mathelin, Christopher Tosh, Wesley Tansey

Proceedings of the 20th Machine Learning in Computational Biology (MLCB), 2025

OneBatchPAM: A Fast and Frugal K-Medoids Algorithm.

Antoine de Mathelin, Nicolas Enrique Cecchi, François Deheeger, Mathilde Mougeot, Nicolas Vayatis

Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence, 2025

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

PhD thesis, 2024

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, Rémi Flamary

CoRR, 2024

2023

Maximum Weight Entropy.

Antoine de Mathelin, François Deheeger, Mathilde Mougeot, Nicolas Vayatis

CoRR, 2023

Deep Anti-Regularized Ensembles provide reliable out-of-distribution uncertainty quantification.

Antoine de Mathelin, François Deheeger, Mathilde Mougeot, Nicolas Vayatis

CoRR, 2023

Personalized One-Shot Collaborative Learning.

Marie Garin, Antoine de Mathelin, Mathilde Mougeot, Nicolas Vayatis

Proceedings of the 35th IEEE International Conference on Tools with Artificial Intelligence, 2023

2022

Fast and Accurate Importance Weighting for Correcting Sample Bias.

Antoine de Mathelin, François Deheeger, Mathilde Mougeot, Nicolas Vayatis

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022

Discrepancy-Based Active Learning for Domain Adaptation.

Antoine de Mathelin, François Deheeger, Mathilde Mougeot, Nicolas Vayatis

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Unsupervised Domain Adaptation for Constraining Star Formation Histories.

Sankalp Gilda, Antoine de Mathelin, Sabine Bellstedt, Guillaume Richard

CoRR, 2021

A Binded VAE for Inorganic Material Generation.

Fouad Oubari, Antoine de Mathelin, Rodrigue Décatoire, Mathilde Mougeot

CoRR, 2021

ADAPT : Awesome Domain Adaptation Python Toolbox.

Antoine de Mathelin, François Deheeger, Guillaume Richard, Mathilde Mougeot, Nicolas Vayatis

CoRR, 2021

Discrepancy-Based Active Learning for Domain Adaptation.

Antoine de Mathelin, Mathilde Mougeot, Nicolas Vayatis

CoRR, 2021

Adversarial Weighting for Domain Adaptation in Regression.

Antoine de Mathelin, Guillaume Richard, François Deheeger, Mathilde Mougeot, Nicolas Vayatis

Proceedings of the 33rd IEEE International Conference on Tools with Artificial Intelligence, 2021

2020

Adversarial Weighting for Domain Adaptation in Regression.

Antoine de Mathelin, Guillaume Richard, Mathilde Mougeot, Nicolas Vayatis

CoRR, 2020

Unsupervised Multi-source Domain Adaptation for Regression.

Guillaume Richard, Antoine de Mathelin, Georges Hébrail, Mathilde Mougeot, Nicolas Vayatis

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2020