Regression with Conditional GAN. - Karan Aggarwal; Matthieu Kirchmeyer; Pranjul Yadav; S. Sathiya Keerthi; Patrick Gallinari (CoRR, 2019)

Unsupervised domain adaptation with non-stochastic missing data. - Matthieu Kirchmeyer; Patrick Gallinari; Alain Rakotomamonjy; Amin Mantrach (Data Min. Knowl. Discov., 2021)

Mapping conditional distributions for domain adaptation under generalized target shift. - Matthieu Kirchmeyer; Alain Rakotomamonjy; Emmanuel de Bézenac; Patrick Gallinari (ICLR, 2022)

Generalizing to New Physical Systems via Context-Informed Dynamics Model. - Matthieu Kirchmeyer; Yuan Yin; Jérémie Donà; Nicolas Baskiotis; Alain Rakotomamonjy; Patrick Gallinari (ICML, 2022)

Diverse Weight Averaging for Out-of-Distribution Generalization. - Alexandre Ramé; Matthieu Kirchmeyer; Thibaud Rahier; Alain Rakotomamonjy; Patrick Gallinari; Matthieu Cord (NeurIPS, 2022)

Continuous PDE Dynamics Forecasting with Implicit Neural Representations. - Yuan Yin; Matthieu Kirchmeyer; Jean-Yves Franceschi; Alain Rakotomamonjy; Patrick Gallinari (ICLR, 2023)

Out-of-distribution Generalization in Deep Learning : Classification and Spatiotemporal Forecasting. (Généralisation hors-distribution en apprentissage profond : classification et prévision spatiotemporelle). - Matthieu Kirchmeyer (2023)

Score-based 3D molecule generation with neural fields. - Matthieu Kirchmeyer; Pedro O. Pinheiro; Saeed Saremi (NeurIPS, 2024)

Antibody DomainBed: Out-of-Distribution Generalization in Therapeutic Protein Design. - Natasa Tagasovska; Ji Won Park; Matthieu Kirchmeyer; Nathan C. Frey; Andrew Martin Watkins; Aya Abdelsalam Ismail; Arian Rokkum Jamasb; Edith Lee; Tyler Bryson; Stephen Ra; Kyunghyun Cho (CoRR, 2024)