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Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisation

arXiv 2006.04296 ↗First posted 8 Jun 2020cs.LG stat.MLcited by 22

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Abstract (arXiv)

In order to improve the performance of Bayesian optimisation, we develop a modified Gaussian process upper confidence bound (GP-UCB) acquisition function. This is done by sampling the exploration-exploitation trade-off parameter from a distribution. We prove that this allows the expected trade-off parameter to be altered to better suit the problem without compromising a bound on the function's Bayesian regret. We also provide results showing that our method achieves better performance than GP-UCB in a range of real-world and synthetic problems.

Key facts. Cited by 22 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Jun 2020.

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Citing papers per year: 2021: 5; 2022: 5; 2023: 4; 2024: 4; 2025: 4.

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  1. 2021Inductive logic programming at 30cited by 47
  2. 2023Looping in the Human Collaborative and Explainable Bayesian Optimizationcited by 27
  3. 2023Randomized Gaussian Process Upper Confidence Bound with Tighter Bayesian Regret Boundscited by 16
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  10. 2025Online inductive learning from answer sets for efficient reinforcement learning explorationcited by 4
  11. 2023Mastering the exploration-exploitation trade-off in Bayesian Optimizationcited by 3
  12. 2024Active Learning for Level Set Estimation Using Randomized Straddle Algorithmscited by 3
10 more
  1. 2022Robust Bayesian optimization with reinforcement learned acquisition functionscited by 2
  2. 2025Wasserstein Barycenter Gaussian Process based Bayesian Optimizationcited by 2
  3. 2021Uncertainty quantification and exploration-exploitation trade-off in humanscited by 1
  4. 2022Atlas: Automate Online Service Configuration in Network Slicingcited by 1
  5. 2024Scalable Knowledge Refactoring using Constrained Optimisationcited by 1
  6. 2021A Unified Batch Selection Policy for Active Metric Learningcited by 0
  7. 2021Learning logic programs by explaining their failurescited by 0
  8. 2022BORA: Bayesian Optimization for Resource Allocationcited by 0
  9. 2025An Empirical Comparison of Cost Functions in Inductive Logic Programmingcited by 0
  10. 2025Honey, I shrunk the hypothesis space (through logical preprocessing)cited by 0