mlsanityData to 7 Oct 2026

Multi-Fidelity Cost-Aware Bayesian Optimization

arXiv 2211.02732 ↗First posted 4 Nov 2022stat.ML stat.MEcited by 20

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Abstract (arXiv)

Bayesian optimization (BO) is increasingly employed in critical applications such as materials design and drug discovery. An increasingly popular strategy in BO is to forgo the sole reliance on high-fidelity data and instead use an ensemble of information sources which provide inexpensive low-fidelity data. The overall premise of this strategy is to reduce the overall sampling costs by querying inexpensive low-fidelity sources whose data are correlated with high-fidelity samples. Here, we propose a multi-fidelity cost-aware BO framework that dramatically outperforms the state-of-the-art technologies in terms of efficiency, consistency, and robustness. We demonstrate the advantages of our framework on analytic and engineering problems and argue that these benefits stem from our two main contributions: (1) we develop a novel acquisition function for multi-fidelity cost-aware BO that safeguards the convergence against the biases of low-fidelity data, and (2) we tailor a newly developed emulator for multi-fidelity BO which enables us to not only simultaneously learn from an ensemble of multi-fidelity datasets, but also identify the severely biased low-fidelity sources that should be excluded from BO.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 20 of its 69 references that are in the map. First posted on arXiv 4 Nov 2022.

Builds on

20 of 69 references.

References · 20

  1. 2018GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Accelerationcited by 603
  2. 2013Gaussian Processes for Big Datacited by 377
  3. 2013Learning to Optimize Via Posterior Samplingcited by 253
  4. 2014Predictive Entropy Search for Efficient Global Optimization of Black-box Functionscited by 201
  5. 2014Bayesian Optimization with Unknown Constraintscited by 109
  6. 2019Practical Multi-fidelity Bayesian Optimization for Hyperparameter Tuningcited by 91
  7. 2019Bayesian Optimization for Materials Design with Mixed Quantitative and Qualitative Variablescited by 64
  8. 2017The reparameterization trick for acquisition functionscited by 61
  9. 2019Bayesian Optimization of Composite Functionscited by 45
  10. 2016Parallel Bayesian Global Optimization of Expensive Functionscited by 43
  11. 2018A Latent Variable Approach to Gaussian Process Modeling with Qualitative and Quantitative Factorscited by 30
  12. 2019BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameterscited by 25
8 more
  1. 2021Latent Map Gaussian Processes for Mixed Variable Metamodelingcited by 23
  2. 2018Group kernels for Gaussian process metamodels with categorical inputscited by 22
  3. 2020Multi-Fidelity Multi-Objective Bayesian Optimization: An Output Space Entropy Search Approachcited by 22
  4. 2021Data Fusion with Latent Map Gaussian Processescited by 16
  5. 2020Multi-fidelity machine-learning with uncertainty quantification and Bayesian optimization for materials design: Application to ternary random alloyscited by 14
  6. 2021Scalable Gaussian Processes for Data-Driven Design using Big Data with Categorical Factorscited by 11
  7. 2021Two-step Lookahead Bayesian Optimization with Inequality Constraintscited by 4
  8. 2017Bayesian Optimization with Automatic Prior Selection for Data-Efficient Direct Policy Searchcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 10; 2025: 3; 2026: 3.

20 papers.

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  1. 2024Operator Learning with Gaussian Processescited by 18
  2. 2024Bayesian optimization as a flexible and efficient design framework for sustainable process systemscited by 15
  3. 2023A Latent Variable Approach for Non-Hierarchical Multi-Fidelity Adaptive Samplingcited by 10
  4. 2025Multifidelity Simulation-based Inference for Computationally Expensive Simulatorscited by 9
  5. 2023Probabilistic Neural Data Fusion for Learning from an Arbitrary Number of Multi-fidelity Data Setscited by 5
  6. 2024Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins via Active Learningcited by 5
  7. 2024Co-orchestration of Multiple Instruments to Uncover Structure-Property Relationships in Combinatorial Librariescited by 4
  8. 2024Interpretable Multi-Source Data Fusion Through Latent Variable Gaussian Processcited by 3
  9. 2024Best Arm Identification with Resource Constraintscited by 3
  10. 2024Unveiling Processing--Property Relationships in Laser Powder Bed Fusion: The Synergy of Machine Learning and High-throughput Experimentscited by 3
  11. 2024Measurements with Noise: Bayesian Optimization for Co-optimizing Noise and Property Discovery in Automated Experimentscited by 3
  12. 2025Constrained multi-fidelity Bayesian optimization with automatic stop conditioncited by 2
8 more
  1. 2025Deep Gaussian Process-based Cost-Aware Batch Bayesian Optimization for Complex Materials Design Campaignscited by 2
  2. 2023On the Effects of Heterogeneous Errors on Multi-fidelity Bayesian Optimizationcited by 1
  3. 2024Heterogenous Multi-Source Data Fusion Through Input Mapping and Latent Variable Gaussian Processcited by 1
  4. 2023Multi-fidelity Design of Porous Microstructures for Thermofluidic Applicationscited by 0
  5. 2024Characterising harmful data sources when constructing multi-fidelity surrogate modelscited by 0
  6. 2026Learning with a Budget: Identifying the Best Arm with Resource Constraintscited by 0
  7. 2026Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regressioncited by 0
  8. 2026On the Brittleness of Maximum Likelihood Estimation for Gaussian Process Hyperparameter Optimizationcited by 0