mlsanityData to 7 Oct 2026

Looping in the Human Collaborative and Explainable Bayesian Optimization

arXiv 2310.17273 ↗First posted 26 Oct 2023cs.LG cs.HC stat.MLcited by 27

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Abstract (arXiv)

Like many optimizers, Bayesian optimization often falls short of gaining user trust due to opacity. While attempts have been made to develop human-centric optimizers, they typically assume user knowledge is well-specified and error-free, employing users mainly as supervisors of the optimization process. We relax these assumptions and propose a more balanced human-AI partnership with our Collaborative and Explainable Bayesian Optimization (CoExBO) framework. Instead of explicitly requiring a user to provide a knowledge model, CoExBO employs preference learning to seamlessly integrate human insights into the optimization, resulting in algorithmic suggestions that resonate with user preference. CoExBO explains its candidate selection every iteration to foster trust, empowering users with a clearer grasp of the optimization. Furthermore, CoExBO offers a no-harm guarantee, allowing users to make mistakes; even with extreme adversarial interventions, the algorithm converges asymptotically to a vanilla Bayesian optimization. We validate CoExBO's efficacy through human-AI teaming experiments in lithium-ion battery design, highlighting substantial improvements over conventional methods. Code is available https://github.com/ma921/CoExBO.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). Builds on 49 of its 100 references that are in the map. First posted on arXiv 26 Oct 2023.

Builds on

49 of 100 references.

References · 49

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,916
  3. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  4. 2017Deep reinforcement learning from human preferencescited by 3,846
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  6. 2014Bayesian Optimization with Unknown Constraintscited by 109
  7. 2019Practical Multi-fidelity Bayesian Optimization for Hyperparameter Tuningcited by 91
  8. 2017Preferential Bayesian Optimizationcited by 72
  9. 2022πBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimizationcited by 69
  10. 2014Input Warping for Bayesian Optimization of Non-stationary Functionscited by 66
  11. 2021Pre-trained Gaussian Processes for Bayesian Optimizationcited by 63
  12. 2018A General Framework for Multi-fidelity Bayesian Optimization with Gaussian Processescited by 52
37 more
  1. 2015Predictive Entropy Search for Bayesian Optimization with Unknown Constraintscited by 51
  2. 2018Dirichlet-based Gaussian Processes for Large-scale Calibrated Classificationcited by 50
  3. 2022Preference Exploration for Efficient Bayesian Optimization with Multiple Outcomescited by 41
  4. 2015GLASSES: Relieving The Myopia Of Bayesian Optimisationcited by 38
  5. 2021RKHS-SHAP: Shapley Values for Kernel Methodscited by 30
  6. 2021Positively Weighted Kernel Quadrature via Subsamplingcited by 25
  7. 2020Sequential Gallery for Interactive Visual Design Optimizationcited by 24
  8. 2021Deconditional Downscaling with Gaussian Processescited by 24
  9. 2020Incorporating Expert Prior in Bayesian Optimisation via Space Warpingcited by 23
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  11. 2023qEUBO: A Decision-Theoretic Acquisition Function for Preferential Bayesian Optimizationcited by 23
  12. 2020Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisationcited by 22
  13. 2019Multi-Attribute Bayesian Optimization With Interactive Preference Learningcited by 21
  14. 2020Projective Preferential Bayesian Optimizationcited by 21
  15. 2021BayesIMP: Uncertainty Quantification for Causal Data Fusioncited by 21
  16. 2022Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombinationcited by 20
  17. 2023Towards Practical Preferential Bayesian Optimization with Skew Gaussian Processescited by 19
  18. 2023Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Modelscited by 19
  19. 2022Multi-Fidelity Bayesian Optimization with Unreliable Information Sourcescited by 17
  20. 2021Autonomous optimization of nonaqueous battery electrolytes via robotic experimentation and machine learningcited by 16
  21. 2023Randomized Gaussian Process Upper Confidence Bound with Tighter Bayesian Regret Boundscited by 16
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 9; 2025: 9; 2026: 9.

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  1. 2025Language-Based Bayesian Optimization Research Assistant (BORA)cited by 13
  2. 2025LLINBO: Trustworthy LLM-in-the-Loop Bayesian Optimizationcited by 13
  3. 2024Explaining Bayesian Optimization by Shapley Values Facilitates Human-AI Collaborationcited by 12
  4. 2024Hyperparameter Importance Analysis for Multi-Objective AutoMLcited by 9
  5. 2024Explainable Bayesian Optimizationcited by 8
  6. 2025PABBO: Preferential Amortized Black-Box Optimizationcited by 7
  7. 2025Exact Shapley Attributions in Quadratic-time for FANOVA Gaussian Processescited by 6
  8. 2024Principled Bayesian Optimisation in Collaboration with Human Expertscited by 5
  9. 2024A Quadrature Approach for General-Purpose Batch Bayesian Optimization via Probabilistic Liftingcited by 3
  10. 2024How Useful is Intermittent, Asynchronous Expert Feedback for Bayesian Optimization?cited by 2
  11. 2024HR-Bandit: Human-AI Collaborated Linear Recourse Banditcited by 2
  12. 2025Comparative Explanations: Explanation Guided Decision Making for Human-in-the-Loop Preference Selectioncited by 2
15 more
  1. 2025BioBO: Biology-informed Bayesian Optimization for Perturbation Designcited by 2
  2. 2025Dynamic Priors in Bayesian Optimization for Hyperparameter Optimizationcited by 2
  3. 2024Anchor-Based Heteroscedastic Noise for Preferential Bayesian Optimizationcited by 1
  4. 2024Building Trust in Black-box Optimization: A Comprehensive Framework for Explainabilitycited by 1
  5. 2025Distilling and exploiting quantitative insights from Large Language Models for enhanced Bayesian optimization of chemical reactionscited by 1
  6. 2026Human-in-the-Loop Meta Bayesian Optimization for Fusion Energy and Scientific Applicationscited by 1
  7. 2026In-Context Black-Box Optimization with Unreliable Feedbackcited by 1
  8. 2026Regret Analysis of Guided Diffusion for Black-Box Optimization over Structured Inputscited by 1
  9. 2025Consecutive Preferential Bayesian Optimizationcited by 0
  10. 2026Dynamic Hyperparameter Importance for Efficient Multi-Objective Optimizationcited by 0
  11. 2026BONSAI: Bayesian Optimization with Natural Simplicity and Interpretabilitycited by 0
  12. 2026Regime-Conditioned Evaluation in Multi-Context Bayesian Optimizationcited by 0
  13. 2026Elicitation-Augmented Bayesian Optimizationcited by 0
  14. 2026A Human-in-the-Loop Bayesian Optimization Framework for Constraint-Aware Bioprocess Developmentcited by 0
  15. 2026Agentic High-Dimensional Bayesian Optimization with Hypothesis- and Evidence-Guided Searchcited by 0