Nonmyopic Multifidelity Active Search

• URL: http://arxiv.org/abs/2106.06356v1
• Date: Fri, 11 Jun 2021 12:55:51 GMT
• Title: Nonmyopic Multifidelity Active Search
• Authors: Quan Nguyen, Arghavan Modiri, Roman Garnett
• Abstract summary: We propose a model of multifidelity active search, as well as a novel, computationally efficient policy for this setting. We evaluate the performance of our solution on real-world datasets and demonstrate significantly better performance than natural benchmarks.
• Score: 15.689830609697685
• License: http://creativecommons.org/licenses/by/4.0/
• 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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