Optimising the Unknown: A Bayesian Approach to Networked Systems
Bayesian optimisation is a powerful machine learning technique for finding the maximum of an unknown, expensive-to-evaluate function. A new study extends this framework to complex networks, where the function’s domain is not a simple Euclidean space but a graph. This presents a unique challenge, as the standard notion of “closeness” for suggesting new evaluation points must be redefined in terms of the network’s structure. The authors propose a novel methodology that leverages the geometry of the underlying graph to guide the sequential search for an optimum.
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