4 papers · 1 filter
Laplacian Kernelized Bandit
Shuang Wu, Arash A. Amini
We study multi-user contextual bandits where users are related by a graph and their reward functions exhibit both non-linear behavior and graph homophily. We introduce a principled…
Simple GNNs with Low Rank Non-parametric Aggregators
Luciano Vinas, Arash A. Amini
We revisit recent spectral GNN approaches to semi-supervised node classification (SSNC). We posit that state-of-the-art (SOTA) GNN architectures may be over-engineered for common S…
Sharp Bounds for Poly-GNNs and the Effect of Graph Noise
Luciano Vinas, Arash A. Amini
We investigate the classification performance of graph neural networks with graph-polynomial features, poly-GNNs, on the problem of semi-supervised node classification. We analyze…
Graph Neural Thompson Sampling
Shuang Wu, Arash A. Amini
We consider an online decision-making problem with a reward function defined over graph-structured data. We formally formulate the problem as an instance of graph action bandit. We…