mlsanityData to 7 Oct 2026

Thinking inside the box: A tutorial on grey-box Bayesian optimization

arXiv 2201.00272 ↗First posted 2 Jan 2022cs.LG math.OC stat.MLcited by 26

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Abstract (arXiv)

Bayesian optimization (BO) is a framework for global optimization of expensive-to-evaluate objective functions. Classical BO methods assume that the objective function is a black box. However, internal information about objective function computation is often available. For example, when optimizing a manufacturing line's throughput with simulation, we observe the number of parts waiting at each workstation, in addition to the overall throughput. Recent BO methods leverage such internal information to dramatically improve performance. We call these "grey-box" BO methods because they treat objective computation as partially observable and even modifiable, blending the black-box approach with so-called "white-box" first-principles knowledge of objective function computation. This tutorial describes these methods, focusing on BO of composite objective functions, where one can observe and selectively evaluate individual constituents that feed into the overall objective; and multi-fidelity BO, where one can evaluate cheaper approximations of the objective function by varying parameters of the evaluation oracle.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 27 of its 68 references that are in the map. First posted on arXiv 2 Jan 2022.

Builds on

27 of 68 references.

References · 27

  1. 2018A Tutorial on Bayesian Optimizationcited by 734
  2. 2015Scalable Bayesian Optimization Using Deep Neural Networkscited by 284
  3. 2017Max-value Entropy Search for Efficient Bayesian Optimizationcited by 202
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15 more
  1. 2020Distributionally Robust Bayesian Optimizationcited by 60
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  4. 2019Bayesian Optimization of Composite Functionscited by 45
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  8. 2020Bayesian Optimization of Risk Measurescited by 35
  9. 2021Bayesian Optimization of Function Networkscited by 33
  10. 2021Bayesian Optimization with High-Dimensional Outputscited by 32
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Citing papers per year: 2022: 4; 2023: 11; 2024: 5; 2025: 4; 2026: 2.

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  2. 2022Model-based Causal Bayesian Optimizationcited by 26
  3. 2022A General Recipe for Likelihood-free Bayesian Optimizationcited by 19
  4. 2024Reinforced In-Context Black-Box Optimizationcited by 14
  5. 2023Bayesian Optimization of Function Networks with Partial Evaluationscited by 10
  6. 2023No-Regret Constrained Bayesian Optimization of Noisy and Expensive Hybrid Models using Differentiable Quantile Function Approximationscited by 6
  7. 2023Joint Composite Latent Space Bayesian Optimizationcited by 5
  8. 2024Efficient Language-instructed Skill Acquisition via Reward-Policy Co-Evolutioncited by 4
  9. 2022Regret Bounds for Noise-Free Cascaded Kernelized Banditscited by 3
  10. 2023Robust Bayesian Target Value Optimizationcited by 3
  11. 2023Generating Transferable Adversarial Simulation Scenarios for Self-Driving via Neural Renderingcited by 3
  12. 2024Causal Bayesian Optimization via Exogenous Distribution Learningcited by 3
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  1. 2024Automated design of nonreciprocal thermal emitters via Bayesian optimizationcited by 3
  2. 2023Scalable Bayesian optimization with high-dimensional outputs using randomized prior networkscited by 2
  3. 2023Trajectory-oriented optimization of stochastic epidemiological modelscited by 2
  4. 2023BOIS: Bayesian Optimization of Interconnected Systemscited by 2
  5. 2025Frozen Layers: Memory-efficient Many-fidelity Hyperparameter Optimizationcited by 2
  6. 2023Long-run Behaviour of Multi-fidelity Bayesian Optimisationcited by 1
  7. 2025A Hierarchical Surrogate Model for Efficient Multi-Task Parameter Learning in Closed-Loop Controlcited by 1
  8. 2023Optimal Observation-Intervention Trade-Off in Optimisation Problems with Causal Structurecited by 0
  9. 2023Grey-box Bayesian Optimization for Sensor Placement in Assisted Living Environmentscited by 0
  10. 2024Constrained composite Bayesian optimization for rational synthesis of polymeric particlescited by 0
  11. 2025Fast Bayesian Optimization of Function Networks with Partial Evaluationscited by 0
  12. 2025Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agentscited by 0
  13. 2026Transferring Information Across Interventions in Causal Bayesian Optimizationcited by 0
  14. 2026Amortizing Scaling Law Construction Costscited by 0