Improved Regret Bounds for Gaussian Process Upper Confidence Bound in Bayesian Optimization
This work provides tighter theoretical guarantees for the GP-UCB algorithm, which is important for researchers and practitioners using Bayesian optimization.
This paper improves the regret bounds for the Gaussian Process Upper Confidence Bound (GP-UCB) algorithm in Bayesian optimization. It shows that GP-UCB achieves $\tilde{O}(\sqrt{T})$ cumulative regret with high probability under a Matérn kernel and $O(\sqrt{T \ln^2 T})$ regret under a squared exponential kernel.
This paper addresses the Bayesian optimization problem (also referred to as the Bayesian setting of the Gaussian process bandit), where the learner seeks to minimize the regret under a function drawn from a known Gaussian process (GP). Under a Matérn kernel with a certain degree of smoothness, we show that the Gaussian process upper confidence bound (GP-UCB) algorithm achieves $\tilde{O}(\sqrt{T})$ cumulative regret with high probability. Furthermore, our analysis yields $O(\sqrt{T \ln^2 T})$ regret under a squared exponential kernel. These results fill the gap between the existing regret upper bound for GP-UCB and the best-known bound provided by Scarlett (2018). The key idea in our proof is to capture the concentration behavior of the input sequence realized by GP-UCB, enabling a more refined analysis of the GP's information gain.