We consider the problem of sequentially optimizing a time-varying objective function using time-varying Bayesian optimization (TVBO). To cope with stale data arising from time variations, current approaches to TVBO require prior knowledge of a constant rate of change. However, in practice, the rate of change is usually unknown. We propose an event-triggered algorithm, ET-GP-UCB, that treats the optimization problem as static until it detects changes in the objective function and then resets the dataset. This allows the algorithm to adapt online to realized temporal changes without the need for exact prior knowledge. The event trigger is based on probabilistic uniform error bounds used in Gaussian process regression. We derive regret bounds of adaptive resets without exact prior knowledge on the temporal changes, and show in numerical experiments that ET-GP-UCB outperforms state-of-the-art algorithms on both synthetic and real-world data. The results demonstrate that ET-GP-UCB is readily applicable to various settings without extensive hyperparameter tuning.
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