mlsanityData to 7 Oct 2026

Triangulation candidates for Bayesian optimization

arXiv 2112.07457 ↗First posted 14 Dec 2021stat.CO cs.LG stat.MLcited by 11

Not on the map. Why?

Abstract (arXiv)

Bayesian optimization involves "inner optimization" over a new-data acquisition criterion which is non-convex/highly multi-modal, may be non-differentiable, or may otherwise thwart local numerical optimizers. In such cases it is common to replace continuous search with a discrete one over random candidates. Here we propose using candidates based on a Delaunay triangulation of the existing input design. We detail the construction of these "tricands" and demonstrate empirically how they outperform both numerically optimized acquisitions and random candidate-based alternatives, and are well-suited for hybrid schemes, on benchmark synthetic and real simulation experiments.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 18 of its 69 references that are in the map. First posted on arXiv 14 Dec 2021.

Builds on

18 of 69 references.

References · 18

  1. 2019Scalable Global Optimization via Local Bayesian Optimizationcited by 359
  2. 2021Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020cited by 146
  3. 2017Doubly Stochastic Variational Inference for Deep Gaussian Processescited by 143
  4. 2015Batch Bayesian Optimization via Local Penalizationcited by 103
  5. 2020Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Searchcited by 87
  6. 2021Multi-Objective Bayesian Optimization over High-Dimensional Search Spacescited by 87
  7. 2021A survey on high-dimensional Gaussian process modeling with application to Bayesian optimizationcited by 76
  8. 2019Bayesian Optimization for Materials Design with Mixed Quantitative and Qualitative Variablescited by 64
  9. 2015GLASSES: Relieving The Myopia Of Bayesian Optimisationcited by 38
  10. 2016Practical heteroskedastic Gaussian process modeling for large simulation experimentscited by 38
  11. 2020Active Learning for Deep Gaussian Process Surrogatescited by 38
  12. 2017Replication or exploration? Sequential design for stochastic simulation experimentscited by 28
6 more
  1. 2018Practical Transfer Learning for Bayesian Optimizationcited by 26
  2. 2019Bayesian Optimization for Policy Search via Online-Offline Experimentationcited by 20
  3. 2016Adaptive Design of Experiments for Conservative Estimation of Excursion Setscited by 10
  4. 2018Contour location via entropy reduction leveraging multiple information sourcescited by 10
  5. 2021Entropy-based adaptive design for contour finding and estimating reliabilitycited by 10
  6. 2018Distance-distributed design for Gaussian process surrogatescited by 7

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 3; 2024: 4; 2025: 3.

11 papers.

  1. 2023Unexpected Improvements to Expected Improvement for Bayesian Optimizationcited by 144
  2. 2022Joint Entropy Search for Multi-objective Bayesian Optimizationcited by 45
  3. 2023qPOTS: Efficient batch multiobjective Bayesian optimization via Pareto optimal Thompson samplingcited by 11
  4. 2025Improving LLM-based Global Optimization with Search Space Partitioningcited by 6
  5. 2023Robust expected improvement for Bayesian optimizationcited by 5
  6. 2024Voronoi Candidates for Bayesian Optimizationcited by 5
  7. 2025Bayesian Optimization with Inexact Acquisition: Is Random Grid Search Sufficient?cited by 4
  8. 2024Targeted Variance Reduction: Robust Bayesian Optimization of Black-Box Simulators with Noise Parameterscited by 2
  9. 2024Expected Diverse Utility (EDU): Diverse Bayesian Optimization of Expensive Computer Simulatorscited by 2
  10. 2024Scalable Batch Bayesian Optimization Via Subspace Acquisition Functionscited by 0
  11. 2025Actively Learning Joint Contours of Multiple Computer Experimentscited by 0