Awesome Bayesian Optimization

A curated repository of awesome Bayesian optimization resources. Maintained by Richard Cornelius Suwandi.

Table of Contents

• Books
• Papers
• Videos
• Blogs
• Software

Books 📚

Title	Author	Year
Bayesian Optimization	Roman Garnett	2023
Bayesian Optimization in Action	Quan Nguyen	2023
Bayesian Optimization: Theory and Practice Using Python	Peng Liu	2023

Papers 📄

Title	Publication	Year
Active Learning and Bayesian Optimization: A Unified Perspective to Learn with a Goal	Springer	2024
Differentiating Policies for Non-Myopic Bayesian Optimization	arXiv	2024
Pre-trained Gaussian Processes for Bayesian Optimization	JMLR	2024
Cost-aware Bayesian optimization via the Pandora's Box Gittins index	arXiv	2024
FunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch	arXiv	2024
Preferential Multi-Objective Bayesian Optimization	arXiv	2024
Approximation-Aware Bayesian Optimization	arXiv	2024
Provably Efficient Bayesian Optimization with Unknown Gaussian Process Hyperparameter Estimation	arXiv	2024
Bayesian Optimization of Functions over Node Subsets in Graphs	arXiv	2024
An adaptive approach to Bayesian Optimization with switching costs	arXiv	2024
Large Language Models to Enhance Bayesian Optimization	ICLR	2024
A Study of Bayesian Neural Network Surrogates for Bayesian Optimization	ICLR	2024
Bayesian Optimization with Conformal Prediction Sets	AISTATS	2023
Sparse Bayesian Optimization	AISTATS	2023
The Behavior and Convergence of Local Bayesian Optimization	NeurIPS	2023
Quantum Bayesian Optimization	NeurIPS	2023
PFNs4BO: In-Context Learning for Bayesian Optimization	ICML	2023
Bayesian Optimization with Informative Covariance	TMLR	2023
Local Bayesian optimization via maximizing probability of descent	NeurIPS	2022
Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders	ICML	2022
Recent Advances in Bayesian Optimization	arXiv	2022
Few-Shot Bayesian Optimization with Deep Kernel Surrogates	ICLR	2021
A survey on high-dimensional Gaussian process modeling with application to Bayesian optimization	arXiv	2021
BoTorch: A framework for efficient Monte-Carlo Bayesian optimization	NeurIPS	2020
Federated Bayesian Optimization via Thompson Sampling	NeurIPS	2020
Why Non-myopic Bayesian Optimization is Promising and How Far Should We Look-ahead? A Study via Rollout	AISTATS	2020
Efficient Computation of Expected Hypervolume Improvement Using Box Decomposition Algorithms	Springer	2019
A Tutorial on Bayesian Optimization	arXiv	2018
Automating Bayesian optimization with Bayesian optimization	NeurIPS	2018
Batched Large-scale Bayesian Optimization in High-dimensional Spaces	AISTATS	2018
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets	AISTATS	2017
Bayesian optimization for automated model selection	NeurIPS	2016
A General Framework for Constrained Bayesian Optimization using Information-based Search	JMLR	2016
GLASSES: Relieving The Myopia Of Bayesian Optimisation	AISTATS	2016
Taking the Human Out of the Loop: A Review of Bayesian Optimization	IEEE Proceedings	2016
High Dimensional Bayesian Optimisation and Bandits via Additive Models	ICML	2015
Scalable Bayesian Optimization Using Deep Neural Networks	ICML	2015
Bayesian optimization for learning gaits under uncertainty	Springer	2015
Bayesian Optimization with Inequality Constraints	ICML	2014
Multi-Task Bayesian Optimization	NeurIPS	2013
Practical Bayesian Optimization of Machine Learning Algorithms	NeurIPS	2012
Entropy Search for Information-Efficient Global Optimization	JMLR	2021
Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design	ICML	2010
Gaussian Processes for Global Optimization	LION3	2008
Efficient Global Optimization of Expensive Black-Box Functions	Springer	1998
Bayesian approach to global optimization	Springer	1989

Videos 🎥

Title	Presenter	Event	Year
Bayesian Optimization	Roman Garnett	Probabilistic Numerics Spring School	2023
A gentle introduction to Bayesian optimization	Sterling Baird	Accelerate Conference, University of Toronto	2023
Bayesian Optimization: Fundamentals, Implementation, and Practice	Quan Nguyen	PyData Global	2022
Bayesian Optimization	Peter Frazier	INFORMS	2018
Bayesian Optimization	Matthew W. Hoffman	UAI	2018
Bayesian Optimization with scikit-learn	Thomas Huijskens	PyData London	2017
Global Optimization with Gaussian Processes	Javier González	Gaussian Process Summer School	2015

Blogs 📝

Title	Author	Platform	Year
Exploring Bayesian Optimization	Apoorv Agnihotri & Nipun Batra	Distill	2020
Bayesian Optimization	Martin Krasser	Personal	2018
Bayesian Optimization with scikit-learn	Thomas Huijskens	Personal	2016

Software 💻

Name	Description
BoTorch	A Python library for Bayesian optimization built on top of PyTorch.
MOE	A Bayesian global optimization engine for optimizing expensive and noisy black-box functions.
Spearmint	An efficient Bayesian optimization package designed for hyperparameter tuning.
GPyOpt	A Bayesian optimization library using Gaussian Processes for optimization tasks. (No longer maintained ⚠️)
Hyperopt	A Python library for optimization over complex search spaces, both serial and parallel.
scikit-optimize	A library for sequential model-based optimization, built on top of popular Python scientific libraries.
Dragonfly	A scalable Bayesian optimization library with a focus on computationally expensive black-box functions.

Contributing

Contributions are welcome! Please feel free to send me pull requests or email (richardsuwandi@link.cuhk.edu.cn).