Improved Regret Analysis for Parallel Gaussian Process Bandit Optimization
This paper studies the regret analysis for parallel Gaussian process (GP) bandit optimization. The known regret upper bounds for the widely used GP batched upper confidence bound and GP batched Thompson sampling (GP-BTS) suffer from a multiplicative factor with respect to the batch size $Q$. To avoid this degradation, existing analyses require a polynomial number of uncertainty sampling (US) for $Q$ at the beginning of optimization. However, this initial US phase is often ineffective in practice. This paper shows that the regret upper bound without the multiplicative factor on $Q$ can be achieved without the initial US phase, using GP-BTS as an example. Furthermore, we show much better re...
| Authors | Shion Takeno, Shogo Iwazaki |
|---|---|
| Primary category | stat.ML |
| Published | 17 August 2026 |
| Last updated at source | 16 September 2026 |
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