Graduate Thesis Or Dissertation

Integrating Resource Planning with Bayesian Optimization

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  • Bayesian optimization (BO) aims to optimize costly-to-evaluate functions by running a limited number of experiments that each evaluates the function at a selected input. Typical BO formulations assume that experiments are selected sequentially, or in fixed batches. Moreover, these experiments can be executed immediately upon request and have the same duration. In the following work, we study two key settings that the current BO framework fails to capture. 1) scenarios where the execution of an experiment requires setup and preparation time, which may vary according to the type of experiment. 2) scenarios where the possibility of parallelism is unbounded and, furthermore, the duration of each experiment can vary. We first define separate BO formulations that model these scenarios. We then propose an algorithm to address each of these problems. Finally, we evaluate the proposed algorithms on a diverse benchmark problems and show that they produce high-quality results compared to a number of natural baselines.
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  • 2018-09-20 to 2019-04-19



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