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arXiv · 2106.06356

Nonmyopic Multifidelity Active Search

Quan Nguyen·Arghavan Modiri·Roman Garnett
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Abstract

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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Quan Nguyen, Arghavan Modiri, Roman Garnett. 2021-06-11. Nonmyopic Multifidelity Active Search. https://arxiv.org/abs/2106.06356

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