AI RESEARCH
Adaptive Candidate Point Thompson Sampling for High-Dimensional Bayesian Optimization
arXiv CS.LG
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ArXi:2604.08891v1 Announce Type: new In Bayesian optimization, Thompson sampling selects the evaluation point by sampling from the posterior distribution over the objective function maximizer. Because this sampling problem is intractable for Gaussian process (GP) surrogates, the posterior distribution is typically restricted to fixed discretizations (i.e., candidate points) that become exponentially sparse as dimensionality increases.