Using Distance Correlation for Efficient Bayesian Optimization
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arXiv
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| Format: | Preprint |
| Published: |
2021
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| _version_ | 1866912377883066368 |
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| author | Kanazawa, Takuya |
| author_facet | Kanazawa, Takuya |
| contents | The need to collect data via expensive measurements of black-box functions is prevalent across science, engineering and medicine. As an example, hyperparameter tuning of a large AI model is critical to its predictive performance but is generally time-consuming and unwieldy. Bayesian optimization (BO) is a collection of methods that aim to address this issue by means of Bayesian statistical inference. In this work, we put forward a BO scheme named BDC, which integrates BO with a statistical measure of association of two random variables called Distance Correlation. BDC balances exploration and exploitation automatically, and requires no manual hyperparameter tuning. We evaluate BDC on a range of benchmark tests and observe that it performs on per with popular BO methods such as the expected improvement and max-value entropy search. We also apply BDC to optimization of sequential integral observations of an unknown terrain and confirm its utility. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2102_08993 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Using Distance Correlation for Efficient Bayesian Optimization Kanazawa, Takuya Machine Learning The need to collect data via expensive measurements of black-box functions is prevalent across science, engineering and medicine. As an example, hyperparameter tuning of a large AI model is critical to its predictive performance but is generally time-consuming and unwieldy. Bayesian optimization (BO) is a collection of methods that aim to address this issue by means of Bayesian statistical inference. In this work, we put forward a BO scheme named BDC, which integrates BO with a statistical measure of association of two random variables called Distance Correlation. BDC balances exploration and exploitation automatically, and requires no manual hyperparameter tuning. We evaluate BDC on a range of benchmark tests and observe that it performs on per with popular BO methods such as the expected improvement and max-value entropy search. We also apply BDC to optimization of sequential integral observations of an unknown terrain and confirm its utility. |
| title | Using Distance Correlation for Efficient Bayesian Optimization |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2102.08993 |