mlsanityData to 7 Oct 2026

Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspaces

arXiv 2304.11468 ↗First posted 22 Apr 2023cs.LG stat.MLcited by 50

Not on the map. Why?

Abstract (arXiv)

Recent advances have extended the scope of Bayesian optimization (BO) to expensive-to-evaluate black-box functions with dozens of dimensions, aspiring to unlock impactful applications, for example, in the life sciences, neural architecture search, and robotics. However, a closer examination reveals that the state-of-the-art methods for high-dimensional Bayesian optimization (HDBO) suffer from degrading performance as the number of dimensions increases or even risk failure if certain unverifiable assumptions are not met. This paper proposes BAxUS that leverages a novel family of nested random subspaces to adapt the space it optimizes over to the problem. This ensures high performance while removing the risk of failure, which we assert via theoretical guarantees. A comprehensive evaluation demonstrates that BAxUS achieves better results than the state-of-the-art methods for a broad set of applications.

Key facts. Cited by 50 papers in the map (data through 7 Oct 2026). Builds on 28 of its 87 references that are in the map. First posted on arXiv 22 Apr 2023.

Builds on

28 of 87 references.

References · 28

  1. 2019Scalable Global Optimization via Local Bayesian Optimizationcited by 359
  2. 2014Sketching as a Tool for Numerical Linear Algebracited by 285
  3. 2018Neural Architecture Search with Bayesian Optimisation and Optimal Transportcited by 192
  4. 2013Bayesian Optimization in a Billion Dimensions via Random Embeddingscited by 178
  5. 2021High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspacescited by 139
  6. 2015High Dimensional Bayesian Optimisation and Bandits via Additive Modelscited by 137
  7. 2016Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasetscited by 131
  8. 2020Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retrainingcited by 121
  9. 2017Batched Large-scale Bayesian Optimization in High-dimensional Spacescited by 116
  10. 2020Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimizationcited by 89
  11. 2020Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Searchcited by 87
  12. 2020Scalable Constrained Bayesian Optimizationcited by 84
16 more
  1. 2015Bayesian optimization for materials designcited by 78
  2. 2022Local Latent Space Bayesian Optimization over Structured Inputscited by 78
  3. 2017Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Spacecited by 76
  4. 2021A survey on high-dimensional Gaussian process modeling with application to Bayesian optimizationcited by 76
  5. 2022πBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimizationcited by 69
  6. 2018Bayesian Optimization of Combinatorial Structurescited by 64
  7. 2021Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spacescited by 51
  8. 2021LassoBench: A High-Dimensional Hyperparameter Optimization Benchmark Suite for Lassocited by 41
  9. 2018Practical Design Space Explorationcited by 35
  10. 2020Gryffin: An algorithm for Bayesian optimization of categorical variables informed by expert knowledgecited by 30
  11. 2017On the choice of the low-dimensional domain for global optimization via random embeddingscited by 25
  12. 2014A warped kernel improving robustness in Bayesian optimization via random embeddingscited by 17
  13. 2017Bayesian Optimization Using Domain Knowledge on the ATRIAS Bipedcited by 17
  14. 2020Semi-supervised Embedding Learning for High-dimensional Bayesian Optimizationcited by 11
  15. 2022Skill-based Multi-objective Reinforcement Learning of Industrial Robot Tasks with Planning and Knowledge Integrationcited by 10
  16. 2019DeepFreak: Learning Crystallography Diffraction Patterns with Automated Machine Learningcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 7; 2024: 13; 2025: 16; 2026: 14.

50 papers.

Sort:

By area:

  1. 2024Vanilla Bayesian Optimization Performs Great in High Dimensionscited by 87
  2. 2024Standard Gaussian Process is All You Need for High-Dimensional Bayesian Optimizationcited by 33
  3. 2024Model-Based Diffusion for Trajectory Optimizationcited by 30
  4. 2023Bounce: Reliable High-Dimensional Bayesian Optimization for Combinatorial and Mixed Spacescited by 27
  5. 2024A survey and benchmark of high-dimensional Bayesian optimization of discrete sequencescited by 22
  6. 2025Understanding High-Dimensional Bayesian Optimizationcited by 19
  7. 2025LLaMEA-BO: A Large Language Model Evolutionary Algorithm for Automatically Generating Bayesian Optimization Algorithmscited by 10
  8. 2025We Still Don't Understand High-Dimensional Bayesian Optimizationcited by 9
  9. 2023Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimationcited by 8
  10. 2025NeST-BO: Fast Local Bayesian Optimization via Newton-Step Targeting of Gradient and Hessian Informationcited by 6
  11. 2023High-Dimensional Bayesian Optimization via Semi-Supervised Learning with Optimized Unlabeled Data Samplingcited by 5
  12. 2023High-dimensional Bayesian Optimization with Group Testingcited by 5
38 more
  1. 2023Joint Composite Latent Space Bayesian Optimizationcited by 5
  2. 2024High-dimensional Bayesian Optimization via Covariance Matrix Adaptation Strategycited by 5
  3. 2026BayeSQP: Bayesian Optimization through Sequential Quadratic Programmingcited by 5
  4. 2023A Bayesian approach for prompt optimization in pre-trained language modelscited by 4
  5. 2024Safe Bayesian Optimization for the Control of High-Dimensional Embodied Systemscited by 4
  6. 2025Posterior Inference in Latent Space for Scalable Constrained Black-box Optimizationcited by 4
  7. 2023Bayesian Optimisation Against Climate Change: Applications and Benchmarkscited by 3
  8. 2024No-Regret Learning of Nash Equilibrium for Black-Box Games via Gaussian Processescited by 3
  9. 2024Regional Expected Improvement for Efficient Trust Region Selection in High-Dimensional Bayesian Optimizationcited by 3
  10. 2024Expected Coordinate Improvement for High-Dimensional Bayesian Optimizationcited by 2
  11. 2024Navigating in High-Dimensional Search Space: A Hierarchical Bayesian Optimization Approachcited by 2
  12. 2024High-Dimensional Bayesian Optimization via Random Projection of Manifold Subspacescited by 2
  13. 2025BARK: A Fully Bayesian Tree Kernel for Black-box Optimizationcited by 2
  14. 2025Enhancing Trust-Region Bayesian Optimization via Newton Methodscited by 2
  15. 2025CoTune: Co-evolutionary Configuration Tuningcited by 2
  16. 2024Batched Energy-Entropy acquisition for Bayesian Optimizationcited by 1
  17. 2025Leveraging Axis-Aligned Subspaces for High-Dimensional Bayesian Optimization with Group Testingcited by 1
  18. 2025Bencher: Simple and Reproducible Benchmarking for Black-Box Optimizationcited by 1
  19. 2025BO4Mob: Bayesian Optimization Benchmarks for High-Dimensional Urban Mobility Problemcited by 1
  20. 2026Adaptive Candidate Point Thompson Sampling for High-Dimensional Bayesian Optimizationcited by 1
  21. 2026BoLT: A Benchmark to Democratize Black-box Optimization Research for Expensive LLM Taskscited by 1
  22. 2026Automated Kernel Discovery Towards Understanding High-dimensional Bayesian Optimizationcited by 1
  23. 2024Bayesian Optimization that Limits Search Region to Lower Dimensions Utilizing Local GPRcited by 0
  24. 2025Modeling All Response Surfaces in One for Conditional Search Spacescited by 0
  25. 2025Pushing the Limits of the Reactive Affine Shaker Algorithm to Higher Dimensionscited by 0
  26. 2025An Adaptive Dropout Approach for High-Dimensional Bayesian Optimizationcited by 0
  27. 2025Scalable neural network-based blackbox optimizationcited by 0
  28. 2025MOCA-HESP: Meta High-dimensional Bayesian Optimization for Combinatorial and Mixed Spaces via Hyper-ellipsoid Partitioningcited by 0
  29. 2026Regime-Adaptive Bayesian Optimization via Dirichlet Process Mixtures of Gaussian Processescited by 0
  30. 2026High-dimensional Level Set Estimation with Trust Regions and Double Acquisition Functionscited by 0
  31. 2026mlr3mbo: Bayesian Optimization in Rcited by 0
  32. 2026Automated Random Embedding for Practical Bayesian Optimization with Unknown Effective Dimensioncited by 0
  33. 2026Pitfalls and Remedies for Multi-Task Bayesian Optimizationcited by 0
  34. 2026Agentic Calibration of Grey-Box Simulation Models: An LLM-Driven Alternativecited by 0
  35. 2026Anchored Scenario Coverage for Failure-Aware First-Hit Batch Inverse Designcited by 0
  36. 2026Bayesian Optimization with Fisher Information Geometry: Gradient Bounds and Trust-Region Methodscited by 0
  37. 2026Agentic High-Dimensional Bayesian Optimization with Hypothesis- and Evidence-Guided Searchcited by 0
  38. 2026Co-PiLOT: Constrained Physics-Informed Latent Optimization for Target-Driven Inverse Designcited by 0