7 citations · 16 across the 22 of their papers we have counts for
5 papers · 1 filter
Generalization of Graph Neural Networks is Robust to Model Mismatch
Zhiyang Wang, Juan Cervino, Alejandro Ribeiro
Graph neural networks (GNNs) have demonstrated their effectiveness in various tasks supported by their generalization capabilities. However, the current analysis of GNN generalizat…
Constrained Learning for Decentralized Multi-Objective Coverage Control
Juan Cervino, Saurav Agarwal, Vijay Kumar +1
The multi-objective coverage control problem requires a robot swarm to collaboratively provide sensor coverage to multiple heterogeneous importance density fields IDFs simultaneous…
Generalization of Geometric Graph Neural Networks with Lipschitz Loss Functions
Zhiyang Wang, Juan Cervino, Alejandro Ribeiro
In this paper, we study the generalization capabilities of geometric graph neural networks (GNNs). We consider GNNs over a geometric graph constructed from a finite set of randomly…
Distributed Training of Large Graph Neural Networks with Variable Communication Rates
Juan Cervino, Md Asadullah Turja, Hesham Mostafa +2
Training Graph Neural Networks (GNNs) on large graphs presents unique challenges due to the large memory and computing requirements. Distributed GNN training, where the graph is pa…
A Manifold Perspective on the Statistical Generalization of Graph Neural Networks
Zhiyang Wang, Juan Cervino, Alejandro Ribeiro
Graph Neural Networks (GNNs) extend convolutional neural networks to operate on graphs. Despite their impressive performances in various graph learning tasks, the theoretical under…