Meta-Learning through Coupled Optimization in Reproducing Kernel Hilbert Spaces. - Juan Cerviño; Juan Andrés Bazerque; Miguel Calvo-Fullana; Alejandro Ribeiro (ACC, 2019)

Parameter Critic: a Model Free Variance Reduction Method Through Imperishable Samples. - Juan Cerviño; Harshat Kumar; Alejandro Ribeiro (CoRR, 2020)

Multi-task Supervised Learning via Cross-learning. - Juan Cerviño; Juan Andrés Bazerque; Miguel Calvo-Fullana; Alejandro Ribeiro (EUSIPCO, 2021)

Increase and Conquer: Training Graph Neural Networks on Growing Graphs. - Juan Cerviño; Luana Ruiz; Alejandro Ribeiro (CoRR, 2021)

Multi-Task Reinforcement Learning in Reproducing Kernel Hilbert Spaces via Cross-Learning. - Juan Cerviño; Juan Andrés Bazerque; Miguel Calvo-Fullana; Alejandro Ribeiro (IEEE Trans. Signal Process., 2021)

Training Stable Graph Neural Networks Through Constrained Learning. - Juan Cerviño; Luana Ruiz; Alejandro Ribeiro (ICASSP, 2022)

An Agnostic Approach to Federated Learning with Class Imbalance. - Zebang Shen; Juan Cerviño; Hamed Hassani; Alejandro Ribeiro (ICLR, 2022)

Federated Representation Learning via Maximal Coding Rate Reduction. - Juan Cerviño; Navid Naderializadeh; Alejandro Ribeiro (CoRR, 2022)

Multi-Task Bias-Variance Trade-Off Through Functional Constraints. - Juan Cerviño; Juan Andrés Bazerque; Miguel Calvo-Fullana; Alejandro Ribeiro (ICASSP, 2023)

Training Graph Neural Networks on Growing Stochastic Graphs. - Juan Cerviño; Luana Ruiz; Alejandro Ribeiro (ICASSP, 2023)

Learning Globally Smooth Functions on Manifolds. - Juan Cerviño; Luiz F. O. Chamon; Benjamin David Haeffele; René Vidal; Alejandro Ribeiro (ICML, 2023)

Intrinsically Motivated Graph Exploration Using Network Theories of Human Curiosity. - Shubhankar Prashant Patankar; Mathieu Ouellet; Juan Cerviño; Alejandro Ribeiro; Kieran A. Murphy; Danielle S. Bassett (LoG, 2023)

FastSample: Accelerating Distributed Graph Neural Network Training for Billion-Scale Graphs. - Hesham Mostafa; Adam Grabowski; Md Asadullah Turja; Juan Cerviño; Alejandro Ribeiro; Nageen Himayat (CoRR, 2023)

Learning by Transference: Training Graph Neural Networks on Growing Graphs. - Juan Cerviño; Luana Ruiz; Alejandro Ribeiro (IEEE Trans. Signal Process., 2023)

Generalization of Graph Neural Networks: Over Geometric Graphs Sampled from Manifolds. - Zhiyang Wang; Juan Cerviño; Alejandro Ribeiro (IEEECONF, 2024)

A Manifold Perspective on the Statistical Generalization of Graph Neural Networks. - Zhiyang Wang; Juan Cerviño; Alejandro Ribeiro (CoRR, 2024)

Generalization of Geometric Graph Neural Networks. - Zhiyang Wang; Juan Cerviño; Alejandro Ribeiro (CoRR, 2024)

Constrained Learning for Decentralized Multi-Objective Coverage Control. - Juan Cerviño; Saurav Agarwal; Vijay Kumar 001; Alejandro Ribeiro (CoRR, 2024)

Generalization of Graph Neural Networks Is Robust to Model Mismatch. - Zhiyang Wang; Juan Cerviño; Alejandro Ribeiro (AAAI, 2025)

Distributed Training of Large Graph Neural Networks With Variable Communication Rates. - Juan Cerviño; Md Asadullah Turja; Hesham Mostafa; Nageen Himayat; Alejandro Ribeiro (IEEE Trans. Signal Inf. Process. over Networks, 2025)

Generalization of Geometric Graph Neural Networks With Lipschitz Loss Functions. - Zhiyang Wang; Juan Cerviño; Alejandro Ribeiro (IEEE Trans. Signal Process., 2025)