BayMOTH: Bayesian optiMizatiOn with meTa-lookahead -- a simple approacH
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914470132973568 |
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| author | Ejaz, Rahman Gopalaswamy, Varchas Luna, Ricardo Lees, Aarne Gundecha, Vineet Kanan, Christopher Sarkar, Soumyendu Betti, Riccardo |
| author_facet | Ejaz, Rahman Gopalaswamy, Varchas Luna, Ricardo Lees, Aarne Gundecha, Vineet Kanan, Christopher Sarkar, Soumyendu Betti, Riccardo |
| contents | Bayesian optimization (BO) has for sequential optimization of expensive black-box functions demonstrated practicality and effectiveness in many real-world settings. Meta-Bayesian optimization (meta-BO) focuses on improving the sample efficiency of BO by making use of information from related tasks. Although meta-BO is sample-efficient when task structure transfers, poor alignment between meta-training and test tasks can cause suboptimal queries to be suggested during online optimization. To this end, we propose a simple meta-BO algorithm that utilizes related-task information when determined useful, falling back to lookahead otherwise, within a unified framework. We demonstrate competitiveness of our method with existing approaches on function optimization tasks, while retaining strong performance in low task-relatedness regimes where test tasks share limited structure with the meta-training set. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_12005 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | BayMOTH: Bayesian optiMizatiOn with meTa-lookahead -- a simple approacH Ejaz, Rahman Gopalaswamy, Varchas Luna, Ricardo Lees, Aarne Gundecha, Vineet Kanan, Christopher Sarkar, Soumyendu Betti, Riccardo Machine Learning Artificial Intelligence Bayesian optimization (BO) has for sequential optimization of expensive black-box functions demonstrated practicality and effectiveness in many real-world settings. Meta-Bayesian optimization (meta-BO) focuses on improving the sample efficiency of BO by making use of information from related tasks. Although meta-BO is sample-efficient when task structure transfers, poor alignment between meta-training and test tasks can cause suboptimal queries to be suggested during online optimization. To this end, we propose a simple meta-BO algorithm that utilizes related-task information when determined useful, falling back to lookahead otherwise, within a unified framework. We demonstrate competitiveness of our method with existing approaches on function optimization tasks, while retaining strong performance in low task-relatedness regimes where test tasks share limited structure with the meta-training set. |
| title | BayMOTH: Bayesian optiMizatiOn with meTa-lookahead -- a simple approacH |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2604.12005 |