GP-BTS removes the batch-size Q factor from regret bounds without initial uncertainty sampling
Shion Takeno and Shogo Iwazaki self-report that in parallel Gaussian process Bayesian optimization, GP-BTS achieves a regret upper bound without the multiplicative batch-size Q factor, with no initial uncertainty sampling stage.
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In parallel Gaussian process Bayesian optimization, GP-BTS achieves a regret upper bound without the multiplicative batch-size Q factor, without the initial uncertainty sampling stage required by existing analyses — and the noiseless bound is tighter. This is a self-reported result by Shion Takeno and Shogo Iwazaki.
Previous analyses required an initial uncertainty sampling stage first, which the authors say is often ineffective in practice.
The conclusion is self-reported by the authors: GP-BTS's regret upper bound carries no multiplicative factor of the batch size Q, and the noiseless bound is tighter.
Boundary: the results are not peer-reviewed, and v2 revised Lemma 4.2 of v1; the preprint was submitted August 17 and revised as v2 on September 16 (arXiv:2608.16492).