Fast Bayesian optimization of Needle-in-a-Haystack problems using zooming memory-based initialization (ZoMBI)
Abstract Needle-in-a-Haystack problems exist across a wide range of applications including rare disease prediction, ecological resource management, fraud detection, and material property optimization. A Needle-in-a-Haystack problem arises when there is an extreme imbalance of optimum conditions rela...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-05-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-023-01048-x |
| _version_ | 1797817851844755456 |
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| author | Alexander E. Siemenn Zekun Ren Qianxiao Li Tonio Buonassisi |
| author_facet | Alexander E. Siemenn Zekun Ren Qianxiao Li Tonio Buonassisi |
| author_sort | Alexander E. Siemenn |
| collection | DOAJ |
| description | Abstract Needle-in-a-Haystack problems exist across a wide range of applications including rare disease prediction, ecological resource management, fraud detection, and material property optimization. A Needle-in-a-Haystack problem arises when there is an extreme imbalance of optimum conditions relative to the size of the dataset. However, current state-of-the-art optimization algorithms are not designed with the capabilities to find solutions to these challenging multidimensional Needle-in-a-Haystack problems, resulting in slow convergence or pigeonholing into a local minimum. In this paper, we present a Zooming Memory-Based Initialization algorithm, entitled ZoMBI, that builds on conventional Bayesian optimization principles to quickly and efficiently optimize Needle-in-a-Haystack problems in both less time and fewer experiments. The ZoMBI algorithm demonstrates compute time speed-ups of 400× compared to traditional Bayesian optimization as well as efficiently discovering optima in under 100 experiments that are up to 3× more highly optimized than those discovered by similar methods. |
| first_indexed | 2024-03-13T09:00:37Z |
| format | Article |
| id | doaj.art-8d8b36af96cf47eda28447ae077d1d41 |
| institution | Directory Open Access Journal |
| issn | 2057-3960 |
| language | English |
| last_indexed | 2024-03-13T09:00:37Z |
| publishDate | 2023-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj.art-8d8b36af96cf47eda28447ae077d1d412023-05-28T11:22:43ZengNature Portfolionpj Computational Materials2057-39602023-05-019111310.1038/s41524-023-01048-xFast Bayesian optimization of Needle-in-a-Haystack problems using zooming memory-based initialization (ZoMBI)Alexander E. Siemenn0Zekun Ren1Qianxiao Li2Tonio Buonassisi3Department of Mechanical Engineering, Massachusetts Institute of TechnologyDepartment of Electrical and Computer Engineering, Singapore-MIT Alliance for Research and TechnologyDepartment of Mathematics, National University of SingaporeDepartment of Mechanical Engineering, Massachusetts Institute of TechnologyAbstract Needle-in-a-Haystack problems exist across a wide range of applications including rare disease prediction, ecological resource management, fraud detection, and material property optimization. A Needle-in-a-Haystack problem arises when there is an extreme imbalance of optimum conditions relative to the size of the dataset. However, current state-of-the-art optimization algorithms are not designed with the capabilities to find solutions to these challenging multidimensional Needle-in-a-Haystack problems, resulting in slow convergence or pigeonholing into a local minimum. In this paper, we present a Zooming Memory-Based Initialization algorithm, entitled ZoMBI, that builds on conventional Bayesian optimization principles to quickly and efficiently optimize Needle-in-a-Haystack problems in both less time and fewer experiments. The ZoMBI algorithm demonstrates compute time speed-ups of 400× compared to traditional Bayesian optimization as well as efficiently discovering optima in under 100 experiments that are up to 3× more highly optimized than those discovered by similar methods.https://doi.org/10.1038/s41524-023-01048-x |
| spellingShingle | Alexander E. Siemenn Zekun Ren Qianxiao Li Tonio Buonassisi Fast Bayesian optimization of Needle-in-a-Haystack problems using zooming memory-based initialization (ZoMBI) npj Computational Materials |
| title | Fast Bayesian optimization of Needle-in-a-Haystack problems using zooming memory-based initialization (ZoMBI) |
| title_full | Fast Bayesian optimization of Needle-in-a-Haystack problems using zooming memory-based initialization (ZoMBI) |
| title_fullStr | Fast Bayesian optimization of Needle-in-a-Haystack problems using zooming memory-based initialization (ZoMBI) |
| title_full_unstemmed | Fast Bayesian optimization of Needle-in-a-Haystack problems using zooming memory-based initialization (ZoMBI) |
| title_short | Fast Bayesian optimization of Needle-in-a-Haystack problems using zooming memory-based initialization (ZoMBI) |
| title_sort | fast bayesian optimization of needle in a haystack problems using zooming memory based initialization zombi |
| url | https://doi.org/10.1038/s41524-023-01048-x |
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