Sharper Regret Bounds for Time-Varying Gaussian Process Bandits with Constant Exploration

We study Bayesian optimization in a time-varying environment where the unknown reward function evolves according to a Gaussian process drift model. Existing GP-UCB analyses in this setting typically require the exploration parameter to grow with the horizon to maintain uniform confidence bounds. Usi...