# Bayesian Optimization for Machine Learning : A Practical Guidebook

arXiv: https://arxiv.org/abs/1612.04858 · HTML: https://mlsanity.com/paper/1612.04858/

## Key facts

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

## Built on by (most cited first, 10 of 10)

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- [Mastering the exploration-exploitation trade-off in Bayesian Optimization](https://mlsanity.com/paper/2305.08624/) (2023)
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- [Mithridates: Auditing and Boosting Backdoor Resistance of Machine Learning Pipelines](https://mlsanity.com/paper/2302.04977/) (2023)
- [A mechanism-driven reinforcement learning framework for shape optimization of airfoils](https://mlsanity.com/paper/2403.04329/) (2024)
- [Sentiment-driven prediction of financial returns: a Bayesian-enhanced FinBERT approach](https://mlsanity.com/paper/2403.04427/) (2024)
- [Structuring a Training Strategy to Robustify Perception Models with Realistic Image Augmentations](https://mlsanity.com/paper/2408.17311/) (2024)
- [HyperZero: A Customized End-to-End Auto-Tuning System for Recommendation with Hourly Feedback](https://mlsanity.com/paper/2501.18126/) (2025)
- [Sparse minimum Redundancy Maximum Relevance for feature selection](https://mlsanity.com/paper/2508.18901/) (2025)

## 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.