mlsanityData to 7 Oct 2026

Transfer Learning with Gaussian Processes for Bayesian Optimization

arXiv 2111.11223 ↗First posted 22 Nov 2021stat.ML cs.AI cs.LGcited by 26

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Abstract (arXiv)

Bayesian optimization is a powerful paradigm to optimize black-box functions based on scarce and noisy data. Its data efficiency can be further improved by transfer learning from related tasks. While recent transfer models meta-learn a prior based on large amount of data, in the low-data regime methods that exploit the closed-form posterior of Gaussian processes (GPs) have an advantage. In this setting, several analytically tractable transfer-model posteriors have been proposed, but the relative advantages of these methods are not well understood. In this paper, we provide a unified view on hierarchical GP models for transfer learning, which allows us to analyze the relationship between methods. As part of the analysis, we develop a novel closed-form boosted GP transfer model that fits between existing approaches in terms of complexity. We evaluate the performance of the different approaches in large-scale experiments and highlight strengths and weaknesses of the different transfer-learning methods.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 9 of its 36 references that are in the map. First posted on arXiv 22 Nov 2021.

Builds on

9 of 36 references.

References · 9

  1. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  2. 2016Multi-Information Source Optimizationcited by 83
  3. 2013A Literature Survey of Benchmark Functions For Global Optimization Problemscited by 75
  4. 2019Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimizationcited by 70
  5. 2019Bayesian Optimization for Materials Design with Mixed Quantitative and Qualitative Variablescited by 64
  6. 2017Virtual vs. Real: Trading Off Simulations and Physical Experiments in Reinforcement Learning with Bayesian Optimizationcited by 33
  7. 2021Emulation of physical processes with Emukitcited by 24
  8. 2018Transferring Knowledge across Learning Processescited by 22
  9. 2018Automatic Exploration of Machine Learning Experiments on OpenMLcited by 13

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 5; 2024: 6; 2025: 7; 2026: 6.

26 papers.

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  1. 2022Recent Advances in Bayesian Optimizationcited by 74
  2. 2023Transfer Learning for Bayesian Optimization: A Surveycited by 27
  3. 2022Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Predictioncited by 21
  4. 2024Efficient Black-box Adversarial Attacks via Bayesian Optimization Guided by a Function Priorcited by 10
  5. 2024Monte Carlo Tree Search based Space Transfer for Black-box Optimizationcited by 7
  6. 2023A Scalable Test Problem Generator for Sequential Transfer Optimizationcited by 5
  7. 2023Scalable Meta-Learning with Gaussian Processescited by 3
  8. 2025Active Alignments of Lens Systems with Reinforcement Learningcited by 3
  9. 2025None To Optima in Few Shots: Bayesian Optimization with MDP Priorscited by 3
  10. 2023MALIBO: Meta-learning for Likelihood-free Bayesian Optimizationcited by 2
  11. 2024Combining additivity and active subspaces for high-dimensional Gaussian process modelingcited by 1
  12. 2024Global Safe Sequential Learning via Efficient Knowledge Transfercited by 1
14 more
  1. 2025Distilling and exploiting quantitative insights from Large Language Models for enhanced Bayesian optimization of chemical reactionscited by 1
  2. 2025Provable Accelerated Bayesian Optimization with Knowledge Transfercited by 1
  3. 2026Multi-Objective Multi-Fidelity Bayesian Optimization with Causal Priorscited by 1
  4. 2026Default Machine Learning Hyperparameters Do Not Provide Informative Initialization for Bayesian Optimizationcited by 1
  5. 2023Data-Efficient Strategies for Probabilistic Voltage Envelopes under Network Contingenciescited by 0
  6. 2024Transfer Learning Bayesian Optimization to Design Competitor DNA Molecules for Use in Diagnostic Assayscited by 0
  7. 2024Sample-Efficient Bayesian Optimization with Transfer Learning for Heterogeneous Search Spacescited by 0
  8. 2025Clustering-based Meta Bayesian Optimization with Theoretical Guaranteecited by 0
  9. 2025Sample-Efficient Bayesian Transfer Learning for Online Machine Parameter Optimizationcited by 0
  10. 2025Bidirectional Information Flow (BIF) - A Sample Efficient Hierarchical Gaussian Process for Bayesian Optimizationcited by 0
  11. 2026An Empirical Study on Ensemble-Based Transfer Learning Bayesian Optimisation with Mixed Variable Typescited by 0
  12. 2026SMOG: Scalable Meta-Learning for Multi-Objective Bayesian Optimizationcited by 0
  13. 2026Weighted Wasserstein Barycenter of Gaussian Processes for exotic Bayesian Optimization taskscited by 0
  14. 2026MF-SCBO : Multi-fidelity Scalable Constrained Bayesian Optimizationcited by 0