Graph Neural Thompson Sampling
<|reference_start|>Graph Neural Thompson Sampling: 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 then propose \texttt{GNN-TS}, a Graph Neural Network (GNN) powered Thompson Sampling (TS) algorithm which employs a GNN approximator for estimating the mean reward function and the graph neural tangent features for uncertainty estimation. We prove that, under certain boundness assumptions on the reward function, GNN-TS achieves a state-of-the-art regret bound which is (1) sub-linear of order $\tilde{\mathcal{O}}((\tilde{d} T)^{1/2})$ in the number of interaction rounds, $T$, and a notion of effective dimension $\tilde{d}$, and (2) independent of the number of graph nodes. Empirical results validate that our proposed \texttt{GNN-TS} exhibits competitive performance and scales well on graph action bandit problems.<|reference_end|>
@article{wu2024graph,
    title={Graph Neural Thompson Sampling},
    author={Shuang Wu, Arash A. Amini},
    journal={arXiv preprint arXiv:2406.10686},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.10686},
    primaryClass={cs.LG cs.AI stat.ML}
}