Near-Optimal Regret Bounds for UCB-Style Gaussian Process Bandits via Data Thinning
Gaussian process upper confidence bound (GP-UCB) has been widely studied, though its known regret upper bound is suboptimal. To obtain near-optimal regret upper bounds, existing studies mainly focus on non-adaptive methods, such as uncertainty sampling, whose practical effectiveness is often limited. This paper shows that, under a Gaussian noise assumption, UCB-style algorithms can achieve near-optimal regret upper bounds via data thinning, a statistical technique for creating independent datasets from a single dataset. First, we show that data thinning can yield tighter confidence intervals in the bandit setting, and that a direct extension of GP-UCB achieves a near-optimal simple regret...
| Authors | Shion Takeno, Shogo Iwazaki, Ichiro Takeuchi |
|---|---|
| Primary category | stat.ML |
| Published | 20 October 2026 |
| Last updated at source | 20 October 2026 |
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