| 2025 | AAAI | Generalization of Graph Neural Networks Is Robust to Model Mismatch. | Zhiyang Wang, Juan Cervio, Alejandro Ribeiro |
| 2025 | ICML | A Manifold Perspective on the Statistical Generalization of Graph Neural Networks. | Zhiyang Wang, Juan Cervio, Alejandro Ribeiro |
| 2025 | ICRA | Constrained Learning for Decentralized Multi-Objective Coverage Control. | Juan Cervio, Saurav Agarwal, Vijay Kumar, Alejandro Ribeiro |
| 2024 | ACSSC | Generalization of Graph Neural Networks: Over Geometric Graphs Sampled from Manifolds. | Zhiyang Wang, Juan Cervio, Alejandro Ribeiro |
| 2023 | ICASSP | Multi-Task Bias-Variance Trade-Off Through Functional Constraints. | Juan Cervio, Juan Andrs Bazerque, Miguel Calvo-Fullana, Alejandro Ribeiro |
| 2023 | ICASSP | Training Graph Neural Networks on Growing Stochastic Graphs. | Juan Cervio, Luana Ruiz, Alejandro Ribeiro |
| 2023 | ICML | Learning Globally Smooth Functions on Manifolds. | Juan Cervio, Luiz F. O. Chamon, Benjamin David Haeffele, Ren Vidal, Alejandro Ribeiro |
| 2022 | ICASSP | Training Stable Graph Neural Networks Through Constrained Learning. | Juan Cervio, Luana Ruiz, Alejandro Ribeiro |
| 2022 | ICLR | An Agnostic Approach to Federated Learning with Class Imbalance. | Zebang Shen, Juan Cervio, Hamed Hassani, Alejandro Ribeiro |