Our latest paper “The Quest for the GRAph Level autoEncoder (GRALE)” was accepted at NeurIPS 2025! arxiv.org/abs/2505.22109 🏆 GRALE 🏆 can encode and decode graphs into and from a shared Euclidean space. Training such a model should require solving the graph matching problem but...

The quest for the GRAph Level autoEncoder (GRALE)

Although graph-based learning has attracted a lot of attention, graph representation learning is still a challenging task whose resolution may impact key application fields such as chemistry or biolog...

arxiv.org

NeurIPS 2030 foreshadowing

11 November 2025.

I am happy to share that our paper "Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs" was accepted at Neurips 2025 ! 🥳 Huge thanks to my co-authors @rflamary.bsky.social and Bertrand Thirion ! arxiv.org/abs/2506.12025 (1/5)

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs

Optimal transport between graphs, based on Gromov-Wasserstein and other extensions, is a powerful tool for comparing and aligning graph structures. However, solving the associated non-convex optimizat...

arxiv.org

We have an accepted paper a at NeurIPS 2024 about supervised graph prediction with transformers and optimal transport loss with Paul Krzakala, Junjie Yang, Florence d’Alché-Buc, Charlotte Laclau and Matthieu Labeau. Details in 🧵 arxiv.org/pdf/2402.12269