Bayesian Optimization & Global Optimization Reading List
Curated by Mouhssine Rifaki | Stanford Electrical Engineering | Last updated August 2026
Bayesian optimization makes expensive black boxes cheap to optimize. This list moves from bandit-style GP-UCB through practical tuning, multi-fidelity and local trust regions, plus the GP systems work that makes it run at scale.
Bayesian Optimization & Global Optimization: 10 key papers
- Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design
Srinivas et al. arXiv 2009.
- Practical Bayesian Optimization of Machine Learning Algorithms
Snoek et al. arXiv 2012.
- A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning
Brochu et al. arXiv 2010.
- Scalable Global Optimization via Local Bayesian Optimization
Eriksson et al. arXiv 2019.
- GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration
Gardner et al. arXiv 2018.
- Taking the Human Out of the Loop: A Review of Bayesian Optimization
Shahriari et al. Proceedings of the IEEE 2016.
- Predictive Entropy Search for Efficient Global Optimization of Black-box Functions
Hernández-Lobato et al. arXiv 2014.
- Max-value Entropy Search for Efficient Bayesian Optimization
Wang and Jegelka. arXiv 2017.
- BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
Balandat et al. arXiv 2019.
- High Dimensional Bayesian Optimisation and Bandits via Additive Models
Kandasamy et al. arXiv 2015.
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