mlsanityData to 14 Oct 2026

Maximizing acquisition functions for Bayesian optimization

arXiv 1805.10196 ↗First posted 25 May 2018stat.ML cs.LGcited by 114

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Abstract (arXiv)

Bayesian optimization is a sample-efficient approach to global optimization that relies on theoretically motivated value heuristics (acquisition functions) to guide its search process. Fully maximizing acquisition functions produces the Bayes' decision rule, but this ideal is difficult to achieve since these functions are frequently non-trivial to optimize. This statement is especially true when evaluating queries in parallel, where acquisition functions are routinely non-convex, high-dimensional, and intractable. We first show that acquisition functions estimated via Monte Carlo integration are consistently amenable to gradient-based optimization. Subsequently, we identify a common family of acquisition functions, including EI and UCB, whose properties not only facilitate but justify use of greedy approaches for their maximization.

Key facts. Cited by 114 papers in the map (data through 14 Oct 2026). First posted on arXiv 25 May 2018.

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Citing papers per year: 2021: 18; 2022: 26; 2023: 18; 2024: 14; 2025: 24; 2026: 14.

114 papers.

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  12. 2021Sequential- and Parallel- Constrained Max-value Entropy Search via Information Lower Boundcited by 23
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  37. 2022MBORE: Multi-objective Bayesian Optimisation by Density-Ratio Estimationcited by 5
  38. 2022PF2ES: Parallel Feasible Pareto Frontier Entropy Search for Multi-Objective Bayesian Optimizationcited by 5

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