AI RESEARCH
Generative Bayesian Optimization: Generative Models as Acquisition Functions
arXiv CS.LG
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ArXi:2510.25240v3 Announce Type: replace-cross We present a general strategy for turning generative models into candidate solution samplers for batch Bayesian optimization (BO). The use of generative models for BO enables large batch scaling as generative sampling, optimization of non-continuous design spaces, and high-dimensional and combinatorial design.