mlsanityData to 14 Oct 2026

Transfer Learning for Bayesian Optimization on Heterogeneous Search Spaces

arXiv 2309.16597 ↗First posted 28 Sep 2023cs.LG cs.AI stat.MLcited by 9

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Abstract (arXiv)

Bayesian optimization (BO) is a popular black-box function optimization method, which makes sequential decisions based on a Bayesian model, typically a Gaussian process (GP), of the function. To ensure the quality of the model, transfer learning approaches have been developed to automatically design GP priors by learning from observations on "training" functions. These training functions are typically required to have the same domain as the "test" function (black-box function to be optimized). In this paper, we introduce MPHD, a model pre-training method on heterogeneous domains, which uses a neural net mapping from domain-specific contexts to specifications of hierarchical GPs. MPHD can be seamlessly integrated with BO to transfer knowledge across heterogeneous search spaces. Our theoretical and empirical results demonstrate the validity of MPHD and its superior performance on challenging black-box function optimization tasks.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 13 of its 38 references that are in the map. First posted on arXiv 28 Sep 2023.

Builds on

13 of 38 references.

References · 13

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2017Max-value Entropy Search for Efficient Bayesian Optimizationcited by 206
  3. 2021Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020cited by 148
  4. 2022Towards Learning Universal Hyperparameter Optimizers with Transformerscited by 81
  5. 2015Bayesian optimization for materials designcited by 80
  6. 2021HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenMLcited by 75
  7. 2021Few-Shot Bayesian Optimization with Deep Kernel Surrogatescited by 71
  8. 2019Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimizationcited by 70
  9. 2021Pre-trained Gaussian Processes for Bayesian Optimizationcited by 63
  10. 2020Learning compositional models of robot skills for task and motion planningcited by 48
  11. 2018Regret bounds for meta Bayesian optimization with an unknown Gaussian process priorcited by 31
  12. 2022Maximum Likelihood Estimation in Gaussian Process Regression is Ill-Posedcited by 16
1 more
  1. 2022HyperBO+: Pre-training a universal prior for Bayesian optimization with hierarchical Gaussian processescited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 3; 2026: 2.

9 papers.

  1. 2025QuestBench: Can LLMs ask the right question to acquire information in reasoning tasks?cited by 63
  2. 2024OmniPred: Language Models as Universal Regressorscited by 34
  3. 2024Language Model Embeddings Can Be Sufficient for Bayesian Optimizationcited by 8
  4. 2024Monte Carlo Tree Search based Space Transfer for Black-box Optimizationcited by 7
  5. 2025Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddingscited by 7
  6. 2025Amortized Multi-Objective Optimization Across Tasks with Generative Solution Modelingcited by 1
  7. 2024Sample-Efficient Bayesian Optimization with Transfer Learning for Heterogeneous Search Spacescited by 0
  8. 2026Transfer Learning in Bayesian Optimization for Aircraft Designcited by 0
  9. 2026Regime-Conditioned Evaluation in Multi-Context Bayesian Optimizationcited by 0