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BOCK : Bayesian Optimization with Cylindrical Kernels

arXiv 1806.01619 ↗First posted 5 Jun 2018stat.ML cs.LGcited by 62

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Abstract (arXiv)

A major challenge in Bayesian Optimization is the boundary issue (Swersky, 2017) where an algorithm spends too many evaluations near the boundary of its search space. In this paper, we propose BOCK, Bayesian Optimization with Cylindrical Kernels, whose basic idea is to transform the ball geometry of the search space using a cylindrical transformation. Because of the transformed geometry, the Gaussian Process-based surrogate model spends less budget searching near the boundary, while concentrating its efforts relatively more near the center of the search region, where we expect the solution to be located. We evaluate BOCK extensively, showing that it is not only more accurate and efficient, but it also scales successfully to problems with a dimensionality as high as 500. We show that the better accuracy and scalability of BOCK even allows optimizing modestly sized neural network layers, as well as neural network hyperparameters.

Key facts. Cited by 62 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Jun 2018.

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Citing papers per year: 2021: 16; 2022: 10; 2023: 10; 2024: 12; 2025: 9; 2026: 5.

62 papers.

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  10. 2023Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddingscited by 24
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  12. 2024A survey and benchmark of high-dimensional Bayesian optimization of discrete sequencescited by 22
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  11. 2021Attentive Neural Processes and Batch Bayesian Optimization for Scalable Calibration of Physics-Informed Digital Twinscited by 5
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  20. 2023A Bayesian Optimization approach for calibrating large-scale activity-based transport modelscited by 3
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  24. 2025Deterministic Global Optimization of the Acquisition Function in Bayesian Optimization: To Do or Not To Do?cited by 3
  25. 2021Procrastinated Tree Search: Black-box Optimization with Delayed, Noisy, and Multi-Fidelity Feedbackcited by 2
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  28. 2023A dynamic Bayesian optimized active recommender system for curiosity-driven Human-in-the-loop automated experimentscited by 2
  29. 2024Expected Coordinate Improvement for High-Dimensional Bayesian Optimizationcited by 2
  30. 2022A model aggregation approach for high-dimensional large-scale optimizationcited by 1
  31. 2024High dimensional Bayesian Optimization via Condensing-Expansion Projectioncited by 1
  32. 2024FastBO: Fast HPO and NAS with Adaptive Fidelity Identificationcited by 1
  33. 2025Bayesian Optimization with Preference Exploration using a Monotonic Neural Network Ensemblecited by 1
  34. 2025BO4Mob: Bayesian Optimization Benchmarks for High-Dimensional Urban Mobility Problemcited by 1
  35. 2025Physically-Constrained Autoencoder-Assisted Bayesian Optimization for Refinement of High-Dimensional Defect-Sensitive Single Crystalline Structurecited by 1
  36. 2026Automated Kernel Discovery Towards Understanding High-dimensional Bayesian Optimizationcited by 1
  37. 2021CobBO: Coordinate Backoff Bayesian Optimization with Two-Stage Kernelscited by 0
  38. 2021Scaling Bayesian Optimization With Game Theorycited by 0

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