Adaptive Candidate Point Thompson Sampling for High-Dimensional Bayesian Optimization
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. While previous works aim to increase candidate point density through scalable GP approximations, our orthogonal approach increases density by adaptively reducing the search space during sampling. Specifically, we introduce Adaptive Candidate Thompson Sampling (ACTS), which generates candidate p...
| Authors | Donney Fan, Geoff Pleiss |
|---|---|
| Primary category | cs.LG |
| Published | 10 April 2026 |
| Last updated at source | 10 April 2026 |
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