Juan Cerviño

Orcid: 0000-0003-2072-7648

According to our database1, Juan Cerviño authored at least 19 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Article 
PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
Constrained Learning for Decentralized Multi-Objective Coverage Control.
CoRR, 2024

Generalization of Geometric Graph Neural Networks.
CoRR, 2024

Generalization of Graph Neural Networks is Robust to Model Mismatch.
CoRR, 2024

Distributed Training of Large Graph Neural Networks with Variable Communication Rates.
CoRR, 2024

A Manifold Perspective on the Statistical Generalization of Graph Neural Networks.
CoRR, 2024

2023
Learning by Transference: Training Graph Neural Networks on Growing Graphs.
IEEE Trans. Signal Process., 2023

FastSample: Accelerating Distributed Graph Neural Network Training for Billion-Scale Graphs.
CoRR, 2023

Intrinsically Motivated Graph Exploration Using Network Theories of Human Curiosity.
Proceedings of the Learning on Graphs Conference, 27-30 November 2023, Virtual Event., 2023

Learning Globally Smooth Functions on Manifolds.
Proceedings of the International Conference on Machine Learning, 2023

Training Graph Neural Networks on Growing Stochastic Graphs.
Proceedings of the IEEE International Conference on Acoustics, 2023

Multi-Task Bias-Variance Trade-Off Through Functional Constraints.
Proceedings of the IEEE International Conference on Acoustics, 2023

2022
Federated Representation Learning via Maximal Coding Rate Reduction.
CoRR, 2022

An Agnostic Approach to Federated Learning with Class Imbalance.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Training Stable Graph Neural Networks Through Constrained Learning.
Proceedings of the IEEE International Conference on Acoustics, 2022

2021
Multi-Task Reinforcement Learning in Reproducing Kernel Hilbert Spaces via Cross-Learning.
IEEE Trans. Signal Process., 2021

Increase and Conquer: Training Graph Neural Networks on Growing Graphs.
CoRR, 2021

Multi-task Supervised Learning via Cross-learning.
Proceedings of the 29th European Signal Processing Conference, 2021

2020
Parameter Critic: a Model Free Variance Reduction Method Through Imperishable Samples.
CoRR, 2020

2019
Meta-Learning through Coupled Optimization in Reproducing Kernel Hilbert Spaces.
Proceedings of the 2019 American Control Conference, 2019


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