Event-Triggered Time-Varying Bayesian Optimization
This addresses the challenge of time-varying optimization in practical settings where change rates are unknown, offering an incremental improvement over prior TVBO methods.
The paper tackles the problem of optimizing time-varying objective functions without prior knowledge of the rate of change, proposing an event-triggered algorithm that adapts online and outperforms existing methods on synthetic and real-world data.
We consider the problem of sequentially optimizing a time-varying objective function using time-varying Bayesian optimization (TVBO). Current approaches to TVBO require prior knowledge of a constant rate of change to cope with stale data arising from time variations. 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 for adaptive resets without exact prior knowledge of the temporal changes and show in numerical experiments that ET-GP-UCB outperforms competing GP-UCB algorithms on both synthetic and real-world data. The results demonstrate that ET-GP-UCB is readily applicable without extensive hyperparameter tuning.