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Nonmyopic Multifidelity Acitve Search

Quan Nguyen · Arghavan Modiri · Roman Garnett

Keywords: [ Applications -> Computer Vision; Deep Learning ] [ Adversarial Networks ] [ Algorithms ] [ Active Learning ] [ Generative Models ]


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.

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