mlsanityData to 7 Oct 2026

BayMOTH: Bayesian optiMizatiOn with meTa-lookahead -- a simple approacH

arXiv 2604.12005 ↗First posted 13 Apr 2026cs.LG cs.AIcited by 0

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Abstract (arXiv)

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.

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References · 18

  1. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  2. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,873
  3. 2016Neural Architecture Search with Reinforcement Learningcited by 1,489
  4. 2018A Tutorial on Bayesian Optimizationcited by 734
  5. 2018Bayesian Model-Agnostic Meta-Learningcited by 194
  6. 2023Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functionscited by 129
  7. 2021HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPOcited by 111
  8. 2019Meta-Learning without Memorizationcited by 92
  9. 2025A Comprehensive Survey of Mixture-of-Experts: Algorithms, Theory, and Applicationscited by 91
  10. 2015Bayesian optimization for materials designcited by 78
  11. 2021HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenMLcited by 75
  12. 2019Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimizationcited by 70
6 more
  1. 2023End-to-End Meta-Bayesian Optimisation with Transformer Neural Processescited by 31
  2. 2023Transfer Learning for Bayesian Optimization: A Surveycited by 27
  3. 2024Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-Optimizationcited by 23
  4. 2021Reinforced Few-Shot Acquisition Function Learning for Bayesian Optimizationcited by 22
  5. 2022Multi-Fidelity Bayesian Optimization with Unreliable Information Sourcescited by 17
  6. 2024EARL-BO: Reinforcement Learning for Multi-Step Lookahead, High-Dimensional Bayesian Optimizationcited by 6

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