Taking the Human Out of the Loop

Shahriari, B., Swersky, K., Wang, Z., Adams, R. P., & de Freitas, N. (2016). Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE, 104(1), 148-175.

Type

Journal article

Intellectual base

Primary research

Year

2016

Volume

104

Issue

1

Pages

148-175

DOI

10.1109/jproc.2015.2494218

DOI registrant

Institute of Electrical and Electronics Engineers

Link

https://doi.org/10.1109/JPROC.2015.2494218

Cited from

ethnography_qualitative_methods

Cited by

1 citation across 1 artifact.

Each citation links to the sentence it supports in the citing article.

Primes

• Theoretical Sampling
  • Adaptive experimental design and Bayesian optimization. The next experiment is chosen to maximize expected information about the parameters of interest given the posterior so far, used in drug discovery, materials science, and policy evaluation.

    This sourceReview of Bayesian optimization, in which an acquisition function selects each next experiment to maximize expected information given the posterior; ported to A/B testing and policy pilots as bandit-based experimentation.

Verification

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