mlsanityData to 14 Oct 2026

The reparameterization trick for acquisition functions

arXiv 1712.00424 ↗First posted 1 Dec 2017stat.ML cs.LG math.OCcited by 63

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Abstract (arXiv)

Bayesian optimization is a sample-efficient approach to solving global optimization problems. Along with a surrogate model, this approach relies on theoretically motivated value heuristics (acquisition functions) to guide the search process. Maximizing acquisition functions yields the best performance; unfortunately, this ideal is difficult to achieve since optimizing acquisition functions per se is frequently non-trivial. This statement is especially true in the parallel setting, where acquisition functions are routinely non-convex, high-dimensional, and intractable. Here, we demonstrate how many popular acquisition functions can be formulated as Gaussian integrals amenable to the reparameterization trick and, ensuingly, gradient-based optimization. Further, we use this reparameterized representation to derive an efficient Monte Carlo estimator for the upper confidence bound acquisition function in the context of parallel selection.

Key facts. Cited by 63 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Dec 2017.

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Citing papers per year: 2021: 3; 2022: 13; 2023: 12; 2024: 19; 2025: 6; 2026: 10.

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