mlsanityData to 7 Oct 2026

Parallel Gaussian Process Optimization with Upper Confidence Bound and Pure Exploration

arXiv 1304.5350 ↗First posted 19 Apr 2013cs.LG stat.MLcited by 50

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Abstract (arXiv)

In this paper, we consider the challenge of maximizing an unknown function f for which evaluations are noisy and are acquired with high cost. An iterative procedure uses the previous measures to actively select the next estimation of f which is predicted to be the most useful. We focus on the case where the function can be evaluated in parallel with batches of fixed size and analyze the benefit compared to the purely sequential procedure in terms of cumulative regret. We introduce the Gaussian Process Upper Confidence Bound and Pure Exploration algorithm (GP-UCB-PE) which combines the UCB strategy and Pure Exploration in the same batch of evaluations along the parallel iterations. We prove theoretical upper bounds on the regret with batches of size K for this procedure which show the improvement of the order of sqrt{K} for fixed iteration cost over purely sequential versions. Moreover, the multiplicative constants involved have the property of being dimension-free. We also confirm empirically the efficiency of GP-UCB-PE on real and synthetic problems compared to state-of-the-art competitors.

Key facts. Cited by 50 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Apr 2013.

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Citing papers per year: 2021: 14; 2022: 10; 2023: 8; 2024: 11; 2025: 1; 2026: 6.

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  29. 2024Batched Energy-Entropy acquisition for Bayesian Optimizationcited by 1
  30. 2025Black-Box Combinatorial Optimization with Order-Invariant Reinforcement Learningcited by 1
  31. 2026Cost-Aware Recovery-Pathway Identification and Bayesian Optimization for Autonomous Materials Discoverycited by 1
  32. 2022Learning to Act: Novel Integration of Algorithms and Models for Epidemic Preparednesscited by 0
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