mlsanityData to 7 Oct 2026

Monte Carlo Tree Search based Space Transfer for Black-box Optimization

arXiv 2412.07186 ↗First posted 10 Dec 2024cs.LG cs.AIcited by 7

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Abstract (arXiv)

Bayesian optimization (BO) is a popular method for computationally expensive black-box optimization. However, traditional BO methods need to solve new problems from scratch, leading to slow convergence. Recent studies try to extend BO to a transfer learning setup to speed up the optimization, where search space transfer is one of the most promising approaches and has shown impressive performance on many tasks. However, existing search space transfer methods either lack an adaptive mechanism or are not flexible enough, making it difficult to efficiently identify promising search space during the optimization process. In this paper, we propose a search space transfer learning method based on Monte Carlo tree search (MCTS), called MCTS-transfer, to iteratively divide, select, and optimize in a learned subspace. MCTS-transfer can not only provide a well-performing search space for warm-start but also adaptively identify and leverage the information of similar source tasks to reconstruct the search space during the optimization process. Experiments on synthetic functions, real-world problems, Design-Bench and hyper-parameter optimization show that MCTS-transfer can demonstrate superior performance compared to other search space transfer methods under different settings. Our code is available at urlhttps://github.com/lamda-bbo/mcts-transfer.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 28 of its 55 references that are in the map. First posted on arXiv 10 Dec 2024.

Builds on

28 of 55 references.

References · 28

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  12. 2021Few-Shot Bayesian Optimization with Deep Kernel Surrogatescited by 70
16 more
  1. 2023PFNs4BO: In-Context Learning for Bayesian Optimizationcited by 70
  2. 2017The reparameterization trick for acquisition functionscited by 61
  3. 2019Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learningcited by 50
  4. 2022Monte Carlo Tree Search based Variable Selection for High Dimensional Bayesian Optimizationcited by 37
  5. 2019AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Searchcited by 33
  6. 2023Transfer Learning for Bayesian Optimization: A Surveycited by 27
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  8. 2019Sample-Efficient Neural Architecture Search by Learning Action Spacecited by 23
  9. 2023Macro Placement by Wire-Mask-Guided Black-Box Optimizationcited by 23
  10. 2020MUMBO: MUlti-task Max-value Bayesian Optimizationcited by 21
  11. 2018Hyperparameter Learning via Distributional Transfercited by 13
  12. 2022Transfer Learning based Search Space Design for Hyperparameter Tuningcited by 13
  13. 2023Transfer Learning for Bayesian Optimization on Heterogeneous Search Spacescited by 9
  14. 2020Additive Tree-Structured Covariance Function for Conditional Parameter Spaces in Bayesian Optimizationcited by 6
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  16. 2024Accelerating Look-ahead in Bayesian Optimization: Multilevel Monte Carlo is All you Needcited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 3.

7 papers.

  1. 2025Policy Guided Tree Search for Enhanced LLM Reasoningcited by 8
  2. 2025Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddingscited by 6
  3. 2025In-Context Multi-Objective Optimizationcited by 5
  4. 2025ChemBOMAS: Accelerated BO in Chemistry with LLM-Enhanced Multi-Agent Systemcited by 3
  5. 2026Diffusion Large Language Models for Black-Box Optimizationcited by 1
  6. 2026Regime-Conditioned Evaluation in Multi-Context Bayesian Optimizationcited by 0
  7. 2026Rethinking Learnability in Offline Data-driven Optimizationcited by 0