Event-Triggered Time-Varying Bayesian Optimization
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...
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Main Authors | , , , |
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Format | Journal Article |
Language | English |
Published |
23.08.2022
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Subjects | |
Online Access | Get full text |
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Summary: | 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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DOI: | 10.48550/arxiv.2208.10790 |