UCSL : A Machine Learning Expectation-Maximization Framework for Unsupervised Clustering Driven by Supervised Learning. - Robin Louiset; Pietro Gori; Benoit Dufumier; Josselin Houenou; Antoine Grigis; Edouard Duchesnay (ECML/PKDD (1), 2021)

Rethinking Positive Sampling for Contrastive Learning with Kernel. - Benoit Dufumier; Carlo Alberto Barbano; Robin Louiset; Edouard Duchesnay; Pietro Gori (CoRR, 2022)

Integrating Prior Knowledge in Contrastive Learning with Kernel. - Benoit Dufumier; Carlo Alberto Barbano; Robin Louiset; Edouard Duchesnay; Pietro Gori (ICML, 2023)

Double InfoGAN for Contrastive Analysis. - Florence Carton; Robin Louiset; Pietro Gori (AISTATS, 2024)

Separating common from salient patterns with Contrastive Representation Learning. - Robin Louiset; Edouard Duchesnay; Antoine Grigis; Pietro Gori (ICLR, 2024)

Supervised Diagnosis Prediction from Cortical Sulci: Toward the Discovery of Neurodevelopmental Biomarkers in Mental Disorders. - Pierre Auriau; Antoine Grigis; Benoit Dufumier; Robin Louiset; Joël Chavas; Pietro Gori; Jean-François Mangin; Edouard Duchesnay (ISBI, 2024)

SepVAE: a contrastive VAE to separate pathological patterns from healthy ones. - Robin Louiset; Edouard Duchesnay; Antoine Grigis; Benoit Dufumier; Pietro Gori (MIDL, 2024)

Exploring the potential of representation and transfer learning for anatomical neuroimaging: Application to psychiatry. - Benoit Dufumier; Pietro Gori; Sara Petiton; Robin Louiset; Jean-François Mangin; Antoine Grigis; Edouard Duchesnay (NeuroImage, 2024)

Learning pathological representations in neuroimaging : Predicting psychiatric diagnosis by integrating heterogeneity constraints. (Apprentissage de représentations pathologiques en neuroimagerie : prédiction du diagnostic psychiatrique en intégrant des contraintes d'hétérogénéité). - Robin Louiset (2024)