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Nonmyopic Multifidelity Acitve Search
Quan Nguyen · Arghavan Modiri · Roman Garnett

Thu Jul 22 09:00 AM -- 11:00 AM (PDT) @ None #None

Active search is a learning paradigm where we seek to identify as many members of a rare, valuable class as possible given a labeling budget. Previous work on active search has assumed access to a faithful (and expensive) oracle reporting experimental results. However, some settings offer access to cheaper surrogates such as computational simulation that may aid in the search. We propose a model of multifidelity active search, as well as a novel, computationally efficient policy for this setting that is motivated by state-of-the-art classical policies. Our policy is nonmyopic and budget aware, allowing for a dynamic tradeoff between exploration and exploitation. We evaluate the performance of our solution on real-world datasets and demonstrate significantly better performance than natural benchmarks.

Author Information

Quan Nguyen (Washington University in St. Louis)
Arghavan Modiri (University of Toronto)
Roman Garnett (Washington University in St. Louis)

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