Juan Cerviño

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 23

Top Topics

Variance Reduction

Dense Subgraphs

Top Venues

IEEE Trans. Signal Process.

Publications

Juan Cerviño, Md Asadullah Turja, Hesham Mostafa, Nageen Himayat, Alejandro Ribeiro
Distributed Training of Large Graph Neural Networks with Variable Communication Rates. CoRR (2024)
Zhiyang Wang, Juan Cerviño, Alejandro Ribeiro
A Manifold Perspective on the Statistical Generalization of Graph Neural Networks. CoRR (2024)
Juan Cerviño, Luiz F. O. Chamon, Benjamin David Haeffele, René Vidal, Alejandro Ribeiro
Learning Globally Smooth Functions on Manifolds. ICML (2023)
Shubhankar P. Patankar, Mathieu Ouellet, Juan Cerviño, Alejandro Ribeiro, Kieran A. Murphy, Dani S. Bassett
Intrinsically motivated graph exploration using network theories of human curiosity. CoRR (2023)
Shubhankar Prashant Patankar, Mathieu Ouellet, Juan Cerviño, Alejandro Ribeiro, Kieran A. Murphy, Danielle S. Bassett
Intrinsically Motivated Graph Exploration Using Network Theories of Human Curiosity. LoG (2023)
Hesham Mostafa, Adam Grabowski, Md Asadullah Turja, Juan Cerviño, Alejandro Ribeiro, Nageen Himayat
FastSample: Accelerating Distributed Graph Neural Network Training for Billion-Scale Graphs. CoRR (2023)
Juan Cerviño, Luana Ruiz, Alejandro Ribeiro
Learning by Transference: Training Graph Neural Networks on Growing Graphs. IEEE Trans. Signal Process. 71 (2023)
Juan Cerviño, Juan Andrés Bazerque, Miguel Calvo-Fullana, Alejandro Ribeiro
Multi-Task Bias-Variance Trade-Off Through Functional Constraints. ICASSP (2023)
Juan Cerviño, Luana Ruiz, Alejandro Ribeiro
Training Graph Neural Networks on Growing Stochastic Graphs. ICASSP (2023)
Zebang Shen, Juan Cerviño, Hamed Hassani, Alejandro Ribeiro
An Agnostic Approach to Federated Learning with Class Imbalance. ICLR (2022)
Juan Cerviño, Luiz F. O. Chamon, Benjamin D. Haeffele, René Vidal, Alejandro Ribeiro
Learning Globally Smooth Functions on Manifolds. CoRR (2022)
Juan Cerviño, Navid Naderializadeh, Alejandro Ribeiro
Federated Representation Learning via Maximal Coding Rate Reduction. CoRR (2022)
Juan Cerviño, Juan Andrés Bazerque, Miguel Calvo-Fullana, Alejandro Ribeiro
Multi-task Bias-Variance Trade-off Through Functional Constraints. CoRR (2022)
Juan Cerviño, Luana Ruiz, Alejandro Ribeiro
Training Graph Neural Networks on Growing Stochastic Graphs. CoRR (2022)
Juan Cerviño, Luana Ruiz, Alejandro Ribeiro
Training Stable Graph Neural Networks Through Constrained Learning. ICASSP (2022)
Juan Cerviño, Juan Andrés Bazerque, Miguel Calvo-Fullana, Alejandro Ribeiro
Multi-task Supervised Learning via Cross-learning. EUSIPCO (2021)
Juan Cerviño, Luana Ruiz, Alejandro Ribeiro
Training Stable Graph Neural Networks Through Constrained Learning. CoRR (2021)
Juan Cerviño, Juan Andrés Bazerque, Miguel Calvo-Fullana, Alejandro Ribeiro
Multi-Task Reinforcement Learning in Reproducing Kernel Hilbert Spaces via Cross-Learning. IEEE Trans. Signal Process. 69 (2021)
Juan Cerviño, Luana Ruiz, Alejandro Ribeiro
Increase and Conquer: Training Graph Neural Networks on Growing Graphs. CoRR (2021)
Juan Cerviño, Harshat Kumar, Alejandro Ribeiro
Parameter Critic: a Model Free Variance Reduction Method Through Imperishable Samples. CoRR (2020)
Juan Cerviño, Juan Andrés Bazerque, Miguel Calvo-Fullana, Alejandro Ribeiro
Multi-task Reinforcement Learning in Reproducing Kernel Hilbert Spaces via Cross-learning. CoRR (2020)
Juan Cerviño, Juan Andrés Bazerque, Miguel Calvo-Fullana, Alejandro Ribeiro
Multi-task Supervised Learning via Cross-learning. CoRR (2020)
Juan Cerviño, Juan Andrés Bazerque, Miguel Calvo-Fullana, Alejandro Ribeiro
Meta-Learning through Coupled Optimization in Reproducing Kernel Hilbert Spaces. ACC (2019)