mlsanityData to 7 Oct 2026

Multi-Fidelity Bayesian Optimization via Deep Neural Networks

arXiv 2007.03117 ↗First posted 6 Jul 2020cs.LG stat.MLcited by 46

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Abstract (arXiv)

Bayesian optimization (BO) is a popular framework to optimize black-box functions. In many applications, the objective function can be evaluated at multiple fidelities to enable a trade-off between the cost and accuracy. To reduce the optimization cost, many multi-fidelity BO methods have been proposed. Despite their success, these methods either ignore or over-simplify the strong, complex correlations across the fidelities, and hence can be inefficient in estimating the objective function. To address this issue, we propose Deep Neural Network Multi-Fidelity Bayesian Optimization (DNN-MFBO) that can flexibly capture all kinds of complicated relationships between the fidelities to improve the objective function estimation and hence the optimization performance. We use sequential, fidelity-wise Gauss-Hermite quadrature and moment-matching to fulfill a mutual information-based acquisition function, which is computationally tractable and efficient. We show the advantages of our method in both synthetic benchmark datasets and real-world applications in engineering design.

Key facts. Cited by 46 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jul 2020.

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Citing papers per year: 2021: 3; 2022: 8; 2023: 11; 2024: 11; 2025: 5; 2026: 8.

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  1. 2023Multi-Resolution Active Learning of Fourier Neural Operatorscited by 22
  2. 2024Active Learning for Neural PDE Solverscited by 22
  3. 2022Multi-Fidelity Bayesian Optimization with Unreliable Information Sourcescited by 17
  4. 2023Scaling Laws for Hyperparameter Optimizationcited by 16
  5. 2022Supervising the Multi-Fidelity Race of Hyperparameter Configurationscited by 15
  6. 2022Multi-fidelity Hierarchical Neural Processescited by 15
  7. 2023Multi-Fidelity Active Learning with GFlowNetscited by 15
  8. 2024Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modelingcited by 13
  9. 2023Disentangled Multi-Fidelity Deep Bayesian Active Learningcited by 11
  10. 2023A Latent Variable Approach for Non-Hierarchical Multi-Fidelity Adaptive Samplingcited by 10
  11. 2024Optimal Multi-Fidelity Best-Arm Identificationcited by 8
  12. 2021Leveraging Trust for Joint Multi-Objective and Multi-Fidelity Optimizationcited by 7
34 more
  1. 2023OVD-Explorer: Optimism Should Not Be the Sole Pursuit of Exploration in Noisy Environmentscited by 7
  2. 2024Best Practices for Multi-Fidelity Bayesian Optimization in Materials and Molecular Researchcited by 7
  3. 2021Batch Multi-Fidelity Bayesian Optimization with Deep Auto-Regressive Networkscited by 6
  4. 2024Multi-Fidelity Methods for Optimization: A Surveycited by 5
  5. 2024Improving Hyperparameter Optimization with Checkpointed Model Weightscited by 5
  6. 2021Amortized Auto-Tuning: Cost-Efficient Bayesian Transfer Optimization for Hyperparameter Recommendationcited by 4
  7. 2022Multi-fidelity Monte Carlo: a pseudo-marginal approachcited by 4
  8. 2024Practical multi-fidelity machine learning: fusion of deterministic and Bayesian modelscited by 4
  9. 2022Cost-effective Framework for Gradual Domain Adaptation with Multifidelitycited by 3
  10. 2022Multifidelity Reinforcement Learning with Control Variatescited by 3
  11. 2023GAR: Generalized Autoregression for Multi-Fidelity Fusioncited by 3
  12. 2022Non-Myopic Multifidelity Bayesian Optimizationcited by 2
  13. 2023Multi-Fidelity Covariance Estimation in the Log-Euclidean Geometrycited by 2
  14. 2024Cost-Sensitive Multi-Fidelity Bayesian Optimization with Transfer of Learning Curve Extrapolationcited by 2
  15. 2026Do We Really Need to Approach the Entire Pareto Front in Many-Objective Bayesian Optimisation?cited by 2
  16. 2023On the Effects of Heterogeneous Errors on Multi-fidelity Bayesian Optimizationcited by 1
  17. 2024Global Safe Sequential Learning via Efficient Knowledge Transfercited by 1
  18. 2024Graph Laplacian-based Bayesian Multi-fidelity Modelingcited by 1
  19. 2025Enhancing Offline Model-Based RL via Active Model Selection: A Bayesian Optimization Perspectivecited by 1
  20. 2025Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modelingcited by 1
  21. 2025Multi-fidelity Bayesian Data-Driven Design of Energy Absorbing Spinodoid Cellular Structurescited by 1
  22. 2026Multi-Objective Multi-Fidelity Bayesian Optimization with Causal Priorscited by 1
  23. 2026Two-Fidelity Best-Action Identification for Stochastic Minimax Treecited by 1
  24. 2022Active Learning with Weak Supervision for Gaussian Processescited by 0
  25. 2023A few-shot graph Laplacian-based approach for improving the accuracy of low-fidelity datacited by 0
  26. 2023Differentiable Multi-Fidelity Fusion: Efficient Learning of Physics Simulations with Neural Architecture Search and Transfer Learningcited by 0
  27. 2024SCORE: A 1D Reparameterization Technique to Break Bayesian Optimization's Curse of Dimensionalitycited by 0
  28. 2025Exploring the Link Between Bayesian Inference and Embodied Intelligence: Toward Open Physical-World Embodied AI Systemscited by 0
  29. 2025Multi-modal Bayesian Neural Network Surrogates with Conjugate Last-Layer Estimationcited by 0
  30. 2026Regime-Adaptive Bayesian Optimization via Dirichlet Process Mixtures of Gaussian Processescited by 0
  31. 2026Self-Tuning Sparse Attention: Multi-Fidelity Hyperparameter Optimization for Transformer Accelerationcited by 0
  32. 2026Multi-Fidelity Flow Matching: Cascaded Refinement of PDE Solutionscited by 0
  33. 2026Transfer Learning Architectures for Scalable Multi-Fidelity Bayesian Optimizationcited by 0
  34. 2026Converge Then Diversify: Decoupling Convergence and Diversity in Multi-Objective Bayesian Optimisationcited by 0