Nonmyopic Multifidelity Acitve Search

Quan Nguyen, Arghavan Modiri, Roman Garnett
Proceedings of the 38th International Conference on Machine Learning, PMLR 139:8109-8118, 2021.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v139-nguyen21f, title = {Nonmyopic Multifidelity Acitve Search}, author = {Nguyen, Quan and Modiri, Arghavan and Garnett, Roman}, booktitle = {Proceedings of the 38th International Conference on Machine Learning}, pages = {8109--8118}, year = {2021}, editor = {Meila, Marina and Zhang, Tong}, volume = {139}, series = {Proceedings of Machine Learning Research}, month = {18--24 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v139/nguyen21f/nguyen21f.pdf}, url = {https://proceedings.mlr.press/v139/nguyen21f.html}, 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.} }
Endnote
%0 Conference Paper %T Nonmyopic Multifidelity Acitve Search %A Quan Nguyen %A Arghavan Modiri %A Roman Garnett %B Proceedings of the 38th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2021 %E Marina Meila %E Tong Zhang %F pmlr-v139-nguyen21f %I PMLR %P 8109--8118 %U https://proceedings.mlr.press/v139/nguyen21f.html %V 139 %X 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.
APA
Nguyen, Q., Modiri, A. & Garnett, R.. (2021). Nonmyopic Multifidelity Acitve Search. Proceedings of the 38th International Conference on Machine Learning, in Proceedings of Machine Learning Research 139:8109-8118 Available from https://proceedings.mlr.press/v139/nguyen21f.html.

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