Exploration strategies for Gaussian process bandit optimization with inference regret guarantees
Existing cumulative regret guarantees for Bayesian optimization (BO) do not necessarily characterize the quality of the algorithm's final output. This gap is critical under noisy evaluations, where the goal is to return a single high-quality recommendation. To address it, we introduce efficient exploration strategies that can be incorporated into any BO procedure by occasionally replacing the acquisition query with an exploration query. To validate this approach, we bound the Gaussian process posterior standard deviation over the search space via the fill distance of the queries, which measures the largest region left unexplored. We then use this result to derive upper bounds on the infer...
| Authors | Hwanwoo Kim, Jungwoo Hong, Kyungjae Lee |
|---|---|
| Primary category | stat.ML |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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