mlsanityData to 14 Oct 2026

Multi-Fidelity Bayesian Optimization with Unreliable Information Sources

arXiv 2210.13937 ↗First posted 25 Oct 2022cs.LG stat.MLcited by 18

Not on the map. Why?

Abstract (arXiv)

Bayesian optimization (BO) is a powerful framework for optimizing black-box, expensive-to-evaluate functions. Over the past decade, many algorithms have been proposed to integrate cheaper, lower-fidelity approximations of the objective function into the optimization process, with the goal of converging towards the global optimum at a reduced cost. This task is generally referred to as multi-fidelity Bayesian optimization (MFBO). However, MFBO algorithms can lead to higher optimization costs than their vanilla BO counterparts, especially when the low-fidelity sources are poor approximations of the objective function, therefore defeating their purpose. To address this issue, we propose rMFBO (robust MFBO), a methodology to make any GP-based MFBO scheme robust to the addition of unreliable information sources. rMFBO comes with a theoretical guarantee that its performance can be bound to its vanilla BO analog, with high controllable probability. We demonstrate the effectiveness of the proposed methodology on a number of numerical benchmarks, outperforming earlier MFBO methods on unreliable sources. We expect rMFBO to be particularly useful to reliably include human experts with varying knowledge within BO processes.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). Builds on 16 of its 33 references that are in the map. First posted on arXiv 25 Oct 2022.

Builds on

16 of 33 references.

References · 16

  1. 2016Automatic chemical design using a data-driven continuous representation of moleculescited by 765
  2. 2018GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Accelerationcited by 609
  3. 2017Max-value Entropy Search for Efficient Bayesian Optimizationcited by 206
  4. 2019Practical Multi-fidelity Bayesian Optimization for Hyperparameter Tuningcited by 93
  5. 2016Multi-Information Source Optimizationcited by 84
  6. 2019ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendationscited by 77
  7. 2022πBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimizationcited by 70
  8. 2019Bayesian Optimization for Materials Design with Mixed Quantitative and Qualitative Variablescited by 66
  9. 2018Adversarially Robust Optimization with Gaussian Processescited by 62
  10. 2018A General Framework for Multi-fidelity Bayesian Optimization with Gaussian Processescited by 52
  11. 2021GIBBON: General-purpose Information-Based Bayesian OptimisatioNcited by 47
  12. 2020Multi-Fidelity Bayesian Optimization via Deep Neural Networkscited by 46
4 more
  1. 2017Virtual vs. Real: Trading Off Simulations and Physical Experiments in Reinforcement Learning with Bayesian Optimizationcited by 34
  2. 2017Practical Bayesian optimization in the presence of outlierscited by 25
  3. 2020Noisy-Input Entropy Search for Efficient Robust Bayesian Optimizationcited by 24
  4. 2021Bias-Robust Bayesian Optimization via Dueling Banditscited by 11

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 3; 2025: 1; 2026: 11.

18 papers.

Sort:

By area:

  1. 2023Looping in the Human Collaborative and Explainable Bayesian Optimizationcited by 27
  2. 2023Multi-Fidelity Active Learning with GFlowNetscited by 15
  3. 2024Best Practices for Multi-Fidelity Bayesian Optimization in Materials and Molecular Researchcited by 7
  4. 2024Multi-Fidelity Methods for Optimization: A Surveycited by 6
  5. 2024Multi-Fidelity Bayesian Optimization With Across-Task Transferable Max-Value Entropy Searchcited by 3
  6. 2026Multi-Objective Multi-Fidelity Bayesian Optimization with Causal Priorscited by 2
  7. 2026In-Context Black-Box Optimization with Unreliable Feedbackcited by 2
  8. 2023Long-run Behaviour of Multi-fidelity Bayesian Optimisationcited by 1
  9. 2026Efficient Controller Learning from Human Preferences and Numerical Data Via Multi-Modal Surrogate Modelscited by 1
  10. 2025Exploring the Link Between Bayesian Inference and Embodied Intelligence: Toward Open Physical-World Embodied AI Systemscited by 0
  11. 2026Weighted Wasserstein Barycenter of Gaussian Processes for exotic Bayesian Optimization taskscited by 0
  12. 2026MAST: A Multi-fidelity Augmented Surrogate model via Spatial Trust-weightingcited by 0
6 more
  1. 2026BayMOTH: Bayesian optiMizatiOn with meTa-lookahead -- a simple approacHcited by 0
  2. 2026Regime-Conditioned Evaluation in Multi-Context Bayesian Optimizationcited by 0
  3. 2026Elicitation-Augmented Bayesian Optimizationcited by 0
  4. 2026Constrained Bayesian Optimisation with Multiple Information Sourcescited by 0
  5. 2026Out-Of-The-Loop Multi-Fidelity Bayesian Optimizationcited by 0
  6. 2026Triage-PiEvo: Evidence Admission for Principle-Evolving Scientific Agentscited by 0