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Bayesian optimization (BO) aims to optimize costly-to-evaluate functions by running a limited number of experiments that each evaluate the function at a selected input. Typical BO formulations assume that experiments are selected one at a time, or in fixed batches, and that experiments can be executed immediately upon request. This setup fails to capture many real-world domains where the execution of an experiment requires setup and preparation time. In this talk, we will present our research on extending the BO setting to incorporate these concerns. The result is a novel BO problem formulation that explicitly models the resources and actions needed to prepare and run experiments. Our algorithmic approach to this problem involves integrating BO principles with a Monte-Carlo tree search. A crucial ingredient is to exploit problem structure in order to design a heuristic function with approximation guarantees that can be used to effectively guide the search. Our experiments demonstrate the effectiveness of this approach and illustrate the more general promise of combining ideas from automated planning and BO.
Alan Fern is a Professor of Computer Science and Associate Head of Research for the School of EECS at Oregon State University. He received his Ph.D. (2004) and M.S. (2000) in computer engineering from Purdue University, and his B.S. (1997) in electrical engineering from the University of Maine. He is an associate editor of the Machine Learning Journal, the Journal of Artificial Intelligence Researce, and serves on the executive council of the International Conference on Automated Planning and Scheduling. His research interests span a range of topics in artificial intelligence, including machine learning and automated planning/control, with a particular interest in the intersection of those areas.