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Bayesian Optimization for Machine Learning : A Practical Guidebook

arXiv 1612.04858 ↗First posted 14 Dec 2016cs.LGcited by 10

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Abstract (arXiv)

The engineering of machine learning systems is still a nascent field; relying on a seemingly daunting collection of quickly evolving tools and best practices. It is our hope that this guidebook will serve as a useful resource for machine learning practitioners looking to take advantage of Bayesian optimization techniques. We outline four example machine learning problems that can be solved using open source machine learning libraries, and highlight the benefits of using Bayesian optimization in the context of these common machine learning applications.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Dec 2016.

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Citing papers per year: 2022: 2; 2023: 2; 2024: 3; 2025: 3.

10 papers.