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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.19241 |
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| _version_ | 1866917101390790656 |
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| author | Stenger, David Lindicke, Armin von Rohr, Alexander Trimpe, Sebastian |
| author_facet | Stenger, David Lindicke, Armin von Rohr, Alexander Trimpe, Sebastian |
| contents | Searching large and complex design spaces for a global optimum can be infeasible and unnecessary. A practical alternative is to iteratively refine the neighborhood of an initial design using local optimization methods such as gradient descent. We propose local entropy search (LES), a Bayesian optimization paradigm that explicitly targets the solutions reachable by the descent sequences of iterative optimizers. The algorithm propagates the posterior belief over the objective through the optimizer, resulting in a probability distribution over descent sequences. It then selects the next evaluation by maximizing mutual information with that distribution, using a combination of analytic entropy calculations and Monte-Carlo sampling of descent sequences. Empirical results on high-complexity synthetic objectives and benchmark problems show that LES achieves strong sample efficiency compared to existing local and global Bayesian optimization methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_19241 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Local Entropy Search over Descent Sequences for Bayesian Optimization Stenger, David Lindicke, Armin von Rohr, Alexander Trimpe, Sebastian Machine Learning Artificial Intelligence Searching large and complex design spaces for a global optimum can be infeasible and unnecessary. A practical alternative is to iteratively refine the neighborhood of an initial design using local optimization methods such as gradient descent. We propose local entropy search (LES), a Bayesian optimization paradigm that explicitly targets the solutions reachable by the descent sequences of iterative optimizers. The algorithm propagates the posterior belief over the objective through the optimizer, resulting in a probability distribution over descent sequences. It then selects the next evaluation by maximizing mutual information with that distribution, using a combination of analytic entropy calculations and Monte-Carlo sampling of descent sequences. Empirical results on high-complexity synthetic objectives and benchmark problems show that LES achieves strong sample efficiency compared to existing local and global Bayesian optimization methods. |
| title | Local Entropy Search over Descent Sequences for Bayesian Optimization |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2511.19241 |