# BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits

arXiv: https://arxiv.org/abs/1405.7430 · HTML: https://mlsanity.com/paper/1405.7430/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2014.

## Built on by (most cited first, 18 of 18)

- [Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow](https://mlsanity.com/paper/2302.08436/) (2023)
- [Revisiting Bayesian Optimization in the light of the COCO benchmark](https://mlsanity.com/paper/2103.16649/) (2021)
- [Scalable First-Order Bayesian Optimization via Structured Automatic Differentiation](https://mlsanity.com/paper/2206.08366/) (2022)
- [X Hacking: The Threat of Misguided AutoML](https://mlsanity.com/paper/2401.08513/) (2024)
- [Designing Optimal Behavioral Experiments Using Machine Learning](https://mlsanity.com/paper/2305.07721/) (2023)
- [Enhanced Bayesian Optimization via Preferential Modeling of Abstract Properties](https://mlsanity.com/paper/2402.17343/) (2024)
- [A Genetic Algorithm with Tree-structured Mutation for Hyperparameter Optimisation of Graph Neural Networks](https://mlsanity.com/paper/2102.11995/) (2021)
- [Looking at the posterior: accuracy and uncertainty of neural-network predictions](https://mlsanity.com/paper/2211.14605/) (2022)
- [Stein Boltzmann Sampling: A Variational Approach for Global Optimization](https://mlsanity.com/paper/2402.04689/) (2024)
- [Bayesian optimization for robust robotic grasping using a sensorized compliant hand](https://mlsanity.com/paper/2410.18237/) (2024)
- [Leveraging semantic similarity for experimentation with AI-generated treatments](https://mlsanity.com/paper/2510.21119/) (2025)
- [MEESO: A Multi-objective End-to-End Self-Optimized Approach for Automatically Building Deep Learning Models](https://mlsanity.com/paper/2211.10921/) (2022)
- [IDToolkit: A Toolkit for Benchmarking and Developing Inverse Design Algorithms in Nanophotonics](https://mlsanity.com/paper/2305.18978/) (2023)
- [Resource-Adaptive Successive Doubling for Hyperparameter Optimization with Large Datasets on High-Performance Computing Systems](https://mlsanity.com/paper/2412.02729/) (2024)
- [Multi-view diffusion geometry using intertwined diffusion trajectories](https://mlsanity.com/paper/2512.01484/) (2025)
- [Practical Efficient Global Optimization is No-regret](https://mlsanity.com/paper/2603.25311/) (2026)
- [mlr3mbo: Bayesian Optimization in R](https://mlsanity.com/paper/2603.29730/) (2026)
- [Beyond Gradient Descent: Adam for Analog Ising Machines](https://mlsanity.com/paper/2606.03917/) (2026)

## Abstract (arXiv)

BayesOpt is a library with state-of-the-art Bayesian optimization methods to solve nonlinear optimization, stochastic bandits or sequential experimental design problems. Bayesian optimization is sample efficient by building a posterior distribution to capture the evidence and prior knowledge for the target function. Built in standard C++, the library is extremely efficient while being portable and flexible. It includes a common interface for C, C++, Python, Matlab and Octave.