Socially Compliant Navigation in dense crowds. - Roman Bresson; Jacques Saraydaryan; Julie Dugdale; Anne Spalanzani (IV, 2019)

Neural Representation and Learning of Hierarchical 2-additive Choquet Integrals. - Roman Bresson; Johanne Cohen; Eyke Hüllermeier; Christophe Labreuche; Michèle Sebag (IJCAI, 2020)

On the Identifiability of Hierarchical Decision Models. - Roman Bresson; Johanne Cohen; Eyke Hüllermeier; Christophe Labreuche; Michèle Sebag (KR, 2021)

Neural learning and validation of hierarchical multi-criteria decision aiding models with interacting criteria. (Apprentissage neuronal et validation de modèles hiérarchiques d'aide à la décision multicritère présentant de l'interaction entre les critères). - Roman Bresson (2022)

Necessary and Sufficient Explanations of Multi-Criteria Decision Aiding Models, with and Without Interacting Criteria. - Christophe Labreuche; Roman Bresson (xAI (2), 2023)

Cutting the Black Box: Conceptual Interpretation of a Deep Neural Net with Multi-Modal Embeddings and Multi-Criteria Decision Aid. - Nicolas Atienza; Roman Bresson; Cyriaque Rousselot; Philippe Caillou; Johanne Cohen; Christophe Labreuche; Michèle Sebag (IJCAI, 2024)

Detect Adversarial Examples with Exchangeability Martingale. - Genghua Dong; Roman Bresson; Henrik Boström (COPA, 2025)

Obtaining Example-Based Explanations from Deep Neural Networks. - Genghua Dong; Henrik Boström; Michalis Vazirgiannis; Roman Bresson (IDA, 2025)

KAGNNs: Kolmogorov-Arnold Networks meet Graph Learning. - Roman Bresson; Giannis Nikolentzos; George Panagopoulos; Michail Chatzianastasis; Jun Pang 001; Michalis Vazirgiannis (Trans. Mach. Learn. Res., 2025)

Prediction via Shapley Value Regression. - Amr Alkhatib; Roman Bresson; Henrik Boström; Michalis Vazirgiannis (CoRR, 2025)