mlsanityData to 13 Oct 2026

Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimation

arXiv 2307.13371 ↗First posted 25 Jul 2023cs.LG stat.MLcited by 8

Not on the map. Why?

Abstract (arXiv)

We study Bayesian optimization (BO) in high-dimensional and non-stationary scenarios. Existing algorithms for such scenarios typically require extensive hyperparameter tuning, which limits their practical effectiveness. We propose a framework, called BALLET, which adaptively filters for a high-confidence region of interest (ROI) as a superlevel-set of a nonparametric probabilistic model such as a Gaussian process (GP). Our approach is easy to tune, and is able to focus on local region of the optimization space that can be tackled by existing BO methods. The key idea is to use two probabilistic models: a coarse GP to identify the ROI, and a localized GP for optimization within the ROI. We show theoretically that BALLET can efficiently shrink the search space, and can exhibit a tighter regret bound than standard BO without ROI filtering. We demonstrate empirically the effectiveness of BALLET on both synthetic and real-world optimization tasks.

Key facts. Cited by 8 papers in the map (data through 13 Oct 2026). Builds on 26 of its 50 references that are in the map. First posted on arXiv 25 Jul 2023.

Builds on

26 of 50 references.

References · 26

  1. 2015Deep Kernel Learningcited by 441
  2. 2019Scalable Global Optimization via Local Bayesian Optimizationcited by 363
  3. 2017Max-value Entropy Search for Efficient Bayesian Optimizationcited by 203
  4. 2013Bayesian Optimization in a Billion Dimensions via Random Embeddingscited by 181
  5. 2015Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP)cited by 176
  6. 2020Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retrainingcited by 122
  7. 2020Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimizationcited by 92
  8. 2020Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Searchcited by 89
  9. 2018Stagewise Safe Bayesian Optimization with Gaussian Processescited by 79
  10. 2015Safe Controller Optimization for Quadrotors with Gaussian Processescited by 77
  11. 2021A survey on high-dimensional Gaussian process modeling with application to Bayesian optimizationcited by 77
  12. 2019Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspacescited by 73
14 more
  1. 2021Few-Shot Bayesian Optimization with Deep Kernel Surrogatescited by 70
  2. 2023Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspacescited by 51
  3. 2016Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimationcited by 40
  4. 2020A Domain-Shrinking based Bayesian Optimization Algorithm with Order-Optimal Regret Performancecited by 39
  5. 2022Monte Carlo Tree Search based Variable Selection for High Dimensional Bayesian Optimizationcited by 37
  6. 2023Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisationcited by 32
  7. 2015Optimization as Estimation with Gaussian Processes in Bandit Settingscited by 17
  8. 2021Risk-averse Heteroscedastic Bayesian Optimizationcited by 17
  9. 2017Adaptive Rate of Convergence of Thompson Sampling for Gaussian Process Optimizationcited by 9
  10. 2016Global Continuous Optimization with Error Bound and Fast Convergencecited by 8
  11. 2020Solving Black-Box Optimization Challenge via Learning Search Space Partition for Local Bayesian Optimizationcited by 7
  12. 2016Advancing Bayesian Optimization: The Mixed-Global-Local (MGL) Kernel and Length-Scale Cool Downcited by 5
  13. 2022Learning Representation for Bayesian Optimization with Collision-free Regularizationcited by 3
  14. 2018Optimizing Photonic Nanostructures via Multi-fidelity Gaussian Processescited by 2

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2023: 1; 2024: 5; 2025: 1; 2026: 1.

8 papers.

  1. 2024Transition Constrained Bayesian Optimization via Markov Decision Processescited by 7
  2. 2024Variational Search Distributionscited by 6
  3. 2024Constrained Multi-objective Bayesian Optimization through Optimistic Constraints Estimationcited by 6
  4. 2024Optimistic Games for Combinatorial Bayesian Optimization with Application to Protein Designcited by 4
  5. 2024No-Regret Learning of Nash Equilibrium for Black-Box Games via Gaussian Processescited by 3
  6. 2023Constrained Bayesian Optimization with Adaptive Active Learning of Unknown Constraintscited by 2
  7. 2025Direct Regret Optimization in Bayesian Optimizationcited by 0
  8. 2026Constraint-Aware Discrete Black-Box Optimization Using Tensor Decompositioncited by 0