A Bayesian Optimization through Sequential Monte Carlo and Statistical Physics-Inspired Techniques
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866912015359934464 |
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| author | Lebedev, Anton Warford, Thomas Şahin, M. Emre |
| author_facet | Lebedev, Anton Warford, Thomas Şahin, M. Emre |
| contents | In this paper, we propose an approach for an application of Bayesian optimization using Sequential Monte Carlo (SMC) and concepts from the statistical physics of classical systems. Our method leverages the power of modern machine learning libraries such as NumPyro and JAX, allowing us to perform Bayesian optimization on multiple platforms, including CPUs, GPUs, TPUs, and in parallel. Our approach enables a low entry level for exploration of the methods while maintaining high performance. We present a promising direction for developing more efficient and effective techniques for a wide range of optimization problems in diverse fields. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_03094 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Bayesian Optimization through Sequential Monte Carlo and Statistical Physics-Inspired Techniques Lebedev, Anton Warford, Thomas Şahin, M. Emre Computation Distributed, Parallel, and Cluster Computing Data Analysis, Statistics and Probability G.3; G.4; D.2.12 In this paper, we propose an approach for an application of Bayesian optimization using Sequential Monte Carlo (SMC) and concepts from the statistical physics of classical systems. Our method leverages the power of modern machine learning libraries such as NumPyro and JAX, allowing us to perform Bayesian optimization on multiple platforms, including CPUs, GPUs, TPUs, and in parallel. Our approach enables a low entry level for exploration of the methods while maintaining high performance. We present a promising direction for developing more efficient and effective techniques for a wide range of optimization problems in diverse fields. |
| title | A Bayesian Optimization through Sequential Monte Carlo and Statistical Physics-Inspired Techniques |
| topic | Computation Distributed, Parallel, and Cluster Computing Data Analysis, Statistics and Probability G.3; G.4; D.2.12 |
| url | https://arxiv.org/abs/2409.03094 |