mlsanityData to 7 Oct 2026

Multi-Objective Bayesian Optimization over High-Dimensional Search Spaces

arXiv 2109.10964 ↗First posted 22 Sep 2021cs.LG cs.AI math.OCcited by 87

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Abstract (arXiv)

Many real world scientific and industrial applications require optimizing multiple competing black-box objectives. When the objectives are expensive-to-evaluate, multi-objective Bayesian optimization (BO) is a popular approach because of its high sample efficiency. However, even with recent methodological advances, most existing multi-objective BO methods perform poorly on search spaces with more than a few dozen parameters and rely on global surrogate models that scale cubically with the number of observations. In this work we propose MORBO, a scalable method for multi-objective BO over high-dimensional search spaces. MORBO identifies diverse globally optimal solutions by performing BO in multiple local regions of the design space in parallel using a coordinated strategy. We show that MORBO significantly advances the state-of-the-art in sample efficiency for several high-dimensional synthetic problems and real world applications, including an optical display design problem and a vehicle design problem with 146 and 222 parameters, respectively. On these problems, where existing BO algorithms fail to scale and perform well, MORBO provides practitioners with order-of-magnitude improvements in sample efficiency over the current approach.

Key facts. Cited by 87 papers in the map (data through 7 Oct 2026). Builds on 26 of its 77 references that are in the map. First posted on arXiv 22 Sep 2021.

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26 of 77 references.

References · 26

  1. 2018Multi-Task Learning as Multi-Objective Optimizationcited by 809
  2. 2018A Tutorial on Bayesian Optimizationcited by 734
  3. 2019Scalable Global Optimization via Local Bayesian Optimizationcited by 359
  4. 2013Bayesian Optimization in a Billion Dimensions via Random Embeddingscited by 178
  5. 2020Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimizationcited by 176
  6. 2021Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020cited by 146
  7. 2021High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspacescited by 139
  8. 2015High Dimensional Bayesian Optimisation and Bandits via Additive Modelscited by 137
  9. 2017Batched Large-scale Bayesian Optimization in High-dimensional Spacescited by 116
  10. 2021Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvementcited by 115
  11. 2018A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizationscited by 98
  12. 2020Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimizationcited by 89
14 more
  1. 2015Predictive Entropy Search for Multi-objective Bayesian Optimizationcited by 88
  2. 2020Scalable Constrained Bayesian Optimizationcited by 84
  3. 2020Max-value Entropy Search for Multi-Objective Bayesian Optimization with Constraintscited by 81
  4. 2019Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspacescited by 73
  5. 2020An Easy-to-use Real-world Multi-objective Optimization Problem Suitecited by 71
  6. 2014Input Warping for Bayesian Optimization of Non-stationary Functionscited by 66
  7. 2018BOCK : Bayesian Optimization with Cylindrical Kernelscited by 62
  8. 2020Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimizationcited by 60
  9. 2019Multi-objective Bayesian Optimization using Pareto-frontier Entropycited by 57
  10. 2020Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimizationcited by 26
  11. 2020Optimizing Coverage and Capacity in Cellular Networks using Machine Learningcited by 21
  12. 2021Latency-Aware Neural Architecture Search with Multi-Objective Bayesian Optimizationcited by 15
  13. 2020Parallel Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraintscited by 12
  14. 2019Efficient Multi-Objective Optimization through Population-based Parallel Surrogate Searchcited by 2

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Citing papers per year: 2021: 2; 2022: 17; 2023: 19; 2024: 10; 2025: 21; 2026: 18.

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  6. 2023Optimizing Privacy, Utility and Efficiency in Constrained Multi-Objective Federated Learningcited by 12
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  10. 2023qPOTS: Efficient batch multiobjective Bayesian optimization via Pareto optimal Thompson samplingcited by 11
  11. 2024Inversion-based Latent Bayesian Optimizationcited by 11
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  15. 2023Combining Multi-Objective Bayesian Optimization with Reinforcement Learning for TinyMLcited by 6
  16. 2024This Too Shall Pass: Removing Stale Observations in Dynamic Bayesian Optimizationcited by 6
  17. 2024Constrained Multi-objective Bayesian Optimization through Optimistic Constraints Estimationcited by 6
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  31. 2025Informed Initialization for Bayesian Optimization and Active Learningcited by 2
  32. 2025OASI: Objective-Aware Surrogate Initialization for Multi-Objective Bayesian Optimization in TinyML Keyword Spottingcited by 2
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  37. 2023A Theoretical Analysis of Efficiency Constrained Utility-Privacy Bi-Objective Optimization in Federated Learningcited by 1
  38. 2024A Trajectory-Based Bayesian Approach to Multi-Objective Hyperparameter Optimization with Epoch-Aware Trade-Offscited by 1

50 most cited of 87 All 87 (JSON)