Multi-fidelity Batch Active Learning for Gaussian Process Classifiers
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912640367853568 |
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| author | Cutforth, Murray Yang, Yiming Fan, Tiffany Guillas, Serge Darve, Eric |
| author_facet | Cutforth, Murray Yang, Yiming Fan, Tiffany Guillas, Serge Darve, Eric |
| contents | Many science and engineering problems rely on expensive computational simulations, where a multi-fidelity approach can accelerate the exploration of a parameter space. We study efficient allocation of a simulation budget using a Gaussian Process (GP) model in the binary simulation output case. This paper introduces Bernoulli Parameter Mutual Information (BPMI), a batch active learning algorithm for multi-fidelity GP classifiers. BPMI circumvents the intractability of calculating mutual information in the probability space by employing a first-order Taylor expansion of the link function. We evaluate BPMI against several baselines on two synthetic test cases and a complex, real-world application involving the simulation of a laser-ignited rocket combustor. In all experiments, BPMI demonstrates superior performance, achieving higher predictive accuracy for a fixed computational budget. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_08865 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-fidelity Batch Active Learning for Gaussian Process Classifiers Cutforth, Murray Yang, Yiming Fan, Tiffany Guillas, Serge Darve, Eric Machine Learning Computational Engineering, Finance, and Science Computational Physics Many science and engineering problems rely on expensive computational simulations, where a multi-fidelity approach can accelerate the exploration of a parameter space. We study efficient allocation of a simulation budget using a Gaussian Process (GP) model in the binary simulation output case. This paper introduces Bernoulli Parameter Mutual Information (BPMI), a batch active learning algorithm for multi-fidelity GP classifiers. BPMI circumvents the intractability of calculating mutual information in the probability space by employing a first-order Taylor expansion of the link function. We evaluate BPMI against several baselines on two synthetic test cases and a complex, real-world application involving the simulation of a laser-ignited rocket combustor. In all experiments, BPMI demonstrates superior performance, achieving higher predictive accuracy for a fixed computational budget. |
| title | Multi-fidelity Batch Active Learning for Gaussian Process Classifiers |
| topic | Machine Learning Computational Engineering, Finance, and Science Computational Physics |
| url | https://arxiv.org/abs/2510.08865 |