mlsanityData to 7 Oct 2026

Combining Multi-Fidelity Modelling and Asynchronous Batch Bayesian Optimization

arXiv 2211.06149 ↗First posted 11 Nov 2022cs.LG cs.CE stat.MLcited by 16

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Abstract (arXiv)

Bayesian Optimization is a useful tool for experiment design. Unfortunately, the classical, sequential setting of Bayesian Optimization does not translate well into laboratory experiments, for instance battery design, where measurements may come from different sources and their evaluations may require significant waiting times. Multi-fidelity Bayesian Optimization addresses the setting with measurements from different sources. Asynchronous batch Bayesian Optimization provides a framework to select new experiments before the results of the prior experiments are revealed. This paper proposes an algorithm combining multi-fidelity and asynchronous batch methods. We empirically study the algorithm behavior, and show it can outperform single-fidelity batch methods and multi-fidelity sequential methods. As an application, we consider designing electrode materials for optimal performance in pouch cells using experiments with coin cells to approximate battery performance.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 28 of its 89 references that are in the map. First posted on arXiv 11 Nov 2022.

Builds on

28 of 89 references.

References · 28

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16 more
  1. 2022Joint Entropy Search for Maximally-Informed Bayesian Optimizationcited by 55
  2. 2019Deep Gaussian Processes for Multi-fidelity Modelingcited by 48
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 8; 2025: 4; 2026: 2.

16 papers.

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  1. 2023Multi-Fidelity Active Learning with GFlowNetscited by 15
  2. 2024Bayesian optimization as a flexible and efficient design framework for sustainable process systemscited by 15
  3. 2023Machine Learning-Assisted Discovery of Flow Reactor Designscited by 8
  4. 2024Transition Constrained Bayesian Optimization via Markov Decision Processescited by 7
  5. 2024Best Practices for Multi-Fidelity Bayesian Optimization in Materials and Molecular Researchcited by 7
  6. 2024BoFire: Bayesian Optimization Framework Intended for Real Experimentscited by 6
  7. 2024Multi-Fidelity Methods for Optimization: A Surveycited by 5
  8. 2025The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine Learningcited by 3
  9. 2025BARK: A Fully Bayesian Tree Kernel for Black-box Optimizationcited by 2
  10. 2025A Guide to Bayesian Optimization in Bioprocess Engineeringcited by 1
  11. 2024Transfer Learning Bayesian Optimization to Design Competitor DNA Molecules for Use in Diagnostic Assayscited by 0
  12. 2024Pessimistic asynchronous sampling in high-cost Bayesian optimizationcited by 0
4 more
  1. 2024Process-constrained batch Bayesian approaches for yield optimization in multi-reactor systemscited by 0
  2. 2025BITS for GAPS: Bayesian Information-Theoretic Sampling for hierarchical GAussian Process Surrogatescited by 0
  3. 2026Bayesian Optimization of Partially Known Systems using Hybrid Modelscited by 0
  4. 2026A Human-in-the-Loop Bayesian Optimization Framework for Constraint-Aware Bioprocess Developmentcited by 0