3 papers
cs.LG2026
The Boolean Power of ReLU
Pablo Barceló, Floris Geerts, Matthias Lanzinger +2
We prove that, on finite simple undirected graphs equipped with a single Boolean node feature, the Boolean queries expressible in -MPLang, for any collection of eventually c…
cs.CL2025
Language Generation: Complexity Barriers and Implications for Learning
Marcelo Arenas, Pablo Barceló, Luis Cofré +1
Kleinberg and Mullainathan showed that language generation in the limit is always possible at the level of computability: given enough positive examples, a learner can eventually g…
cs.LG2025
Message Passing on the Edge: Towards Scalable and Expressive GNNs
Pablo Barceló, Fabian Jogl, Alexander Kozachinskiy +3
Graph neural networks (GNNs) are widely used in graph learning and most architectures propagate information by passing messages between vertices. In this work, we shift our attenti…