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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2021
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2109.09264 |
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| _version_ | 1866917587802128384 |
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| author | Shen, Yihang Kingsford, Carl |
| author_facet | Shen, Yihang Kingsford, Carl |
| contents | Bayesian Optimization (BO) is a method for globally optimizing black-box functions. While BO has been successfully applied to many scenarios, developing effective BO algorithms that scale to functions with high-dimensional domains is still a challenge. Optimizing such functions by vanilla BO is extremely time-consuming. Alternative strategies for high-dimensional BO that are based on the idea of embedding the high-dimensional space to the one with low dimension are sensitive to the choice of the embedding dimension, which needs to be pre-specified. We develop a new computationally efficient high-dimensional BO method that exploits variable selection. Our method is able to automatically learn axis-aligned sub-spaces, i.e. spaces containing selected variables, without the demand of any pre-specified hyperparameters. We theoretically analyze the computational complexity of our algorithm and derive the regret bound. We empirically show the efficacy of our method on several synthetic and real problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2109_09264 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Computationally Efficient High-Dimensional Bayesian Optimization via Variable Selection Shen, Yihang Kingsford, Carl Machine Learning Bayesian Optimization (BO) is a method for globally optimizing black-box functions. While BO has been successfully applied to many scenarios, developing effective BO algorithms that scale to functions with high-dimensional domains is still a challenge. Optimizing such functions by vanilla BO is extremely time-consuming. Alternative strategies for high-dimensional BO that are based on the idea of embedding the high-dimensional space to the one with low dimension are sensitive to the choice of the embedding dimension, which needs to be pre-specified. We develop a new computationally efficient high-dimensional BO method that exploits variable selection. Our method is able to automatically learn axis-aligned sub-spaces, i.e. spaces containing selected variables, without the demand of any pre-specified hyperparameters. We theoretically analyze the computational complexity of our algorithm and derive the regret bound. We empirically show the efficacy of our method on several synthetic and real problems. |
| title | Computationally Efficient High-Dimensional Bayesian Optimization via Variable Selection |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2109.09264 |