  We propose practical extensions to Bayesian optimization for solving dynamic
problems. We model dynamic objective functions using spatiotemporal Gaussian
process priors which capture all the instances of the functions over time. Our
extensions to Bayesian optimization use the information learnt from this model
to guide the tracking of a temporally evolving minimum. By exploiting temporal
correlations, the proposed method also determines when to make evaluations, how
fast to make those evaluations, and it induces an appropriate budget of steps
based on the available information. Lastly, we evaluate our technique on
synthetic and real-world problems.
