Getting Our Bearings: Advances in Understanding Spatial Reorientation. - Nora S. Newcombe; Alexandre Duval; Sang Ah Lee; Anna Shusterman; Noam Miller (CogSci, 2020)

Breaking Writer's Block: Low-cost Fine-tuning of Natural Language Generation Models. - Alexandre Duval; Thomas Lamson; Gael de Leseleuc de Kerouara; Matthias Gallé (EACL (System Demonstrations), 2021)

GraphSVX: Shapley Value Explanations for Graph Neural Networks. - Alexandre Duval; Fragkiskos D. Malliaros (ECML/PKDD (2), 2021)

Controllable and contextualised writing tool for novel authors. - Alexandre Duval; Gael de Leseleuc de Kerouara; Thomas Lamson (CoRR, 2021)

Higher-order Clustering and Pooling for Graph Neural Networks. - Alexandre Duval; Fragkiskos D. Malliaros (CIKM, 2022)

FAENet: Frame Averaging Equivariant GNN for Materials Modeling. - Alexandre Duval; Victor Schmidt; Alex Hernández-García; Santiago Miret; Fragkiskos D. Malliaros; Yoshua Bengio; David Rolnick (ICML, 2023)

The Second Learning on Graphs Conference: Preface. - Soledad Villar; Benjamin Paul Chamberlain; Yuanqi Du; Hannes Stärk; Chaitanya K. Joshi; Andreea Deac; Iulia Duta; Joshua Robinson 001; Yanqiao Zhu 001; Kexin Huang; Michelle M. Li; Sofia Bourhim; et al. (LoG, 2023)

The eyes and hearts of UAV pilots: observations of physiological responses in real-life scenarios. - Alexandre Duval; Anita Paas; Abdalwhab Abdalwhab; David St-Onge (RO-MAN, 2023)

Crystal-GFN: sampling crystals with desirable properties and constraints. - Alex Hernández-García; Alexandre Duval; Alexandra Volokhova; Yoshua Bengio; Divya Sharma; Pierre Luc Carrier; Michal Koziarski; Victor Schmidt (CoRR, 2023)

On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions. - Alvaro Carbonero; Alexandre Duval; Victor Schmidt; Santiago Miret; Alex Hernández-García; Yoshua Bengio; David Rolnick (CoRR, 2023)

A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems. - Alexandre Duval; Simon V. Mathis; Chaitanya K. Joshi; Victor Schmidt; Santiago Miret; Fragkiskos D. Malliaros; Taco Cohen; Pietro Lio; Yoshua Bengio; Michael M. Bronstein (CoRR, 2023)

Improving Molecular Modeling with Geometric GNNs: an Empirical Study. - Ali Ramlaoui; Théo Saulus; Basile Terver; Victor Schmidt; David Rolnick; Fragkiskos D. Malliaros; Alexandre Duval (CoRR, 2024)

PhAST: Physics-Aware, Scalable, and Task-Specific GNNs for Accelerated Catalyst Design. - Alexandre Duval; Victor Schmidt; Santiago Miret; Yoshua Bengio; Alex Hernández-García; David Rolnick (J. Mach. Learn. Res., 2024)

A Graph Machine Learning Journey: from Explainability to Climate Action. (Apprentissage Automatique sur Graphes: de l'Explicabilité à l'Action Climatique). - Alexandre Duval (2024)