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Global Optimization on Graph-Structured Data via Gaussian Processes with Spectral Representations
Bayesian optimization (BO) is a powerful framework for optimizing expensive black-box objectives, yet extending it to graph-structured domains remains challenging due to the discrete and combinatorial nature of graphs. Existing approaches often rely either on full graph topology, which is impractica
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