# OpenBox: A Generalized Black-box Optimization Service

arXiv: https://arxiv.org/abs/2106.00421 · HTML: https://mlsanity.com/paper/2106.00421/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 51 references that are in the map.
- First posted on arXiv 1 Jun 2021.

## Builds on (most cited first, 18 of 18)

- [Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization](https://mlsanity.com/paper/1603.06560/) (2016)
- [BOHB: Robust and Efficient Hyperparameter Optimization at Scale](https://mlsanity.com/paper/1807.01774/) (2018)
- [Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization](https://mlsanity.com/paper/2006.05078/) (2020)
- [Variational Bayesian Optimal Experimental Design](https://mlsanity.com/paper/1903.05480/) (2019)
- [Batch Bayesian Optimization via Local Penalization](https://mlsanity.com/paper/1505.08052/) (2015)
- [A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations](https://mlsanity.com/paper/1805.12168/) (2018)
- [Multi-Information Source Optimization](https://mlsanity.com/paper/1603.00389/) (2016)
- [Scalable Constrained Bayesian Optimization](https://mlsanity.com/paper/2002.08526/) (2020)
- [Max-value Entropy Search for Multi-Objective Bayesian Optimization with Constraints](https://mlsanity.com/paper/2009.01721/) (2020)
- [Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictions](https://mlsanity.com/paper/1410.7827/) (2014)
- [Predictive Entropy Search for Bayesian Optimization with Unknown Constraints](https://mlsanity.com/paper/1502.05312/) (2015)
- [A General Framework for Constrained Bayesian Optimization using Information-based Search](https://mlsanity.com/paper/1511.09422/) (2015)
- [Fair Bayesian Optimization](https://mlsanity.com/paper/2006.05109/) (2020)
- [Efficient Automatic CASH via Rising Bandits](https://mlsanity.com/paper/2012.04371/) (2020)
- [Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian](https://mlsanity.com/paper/1605.09466/) (2016)
- [Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints](https://mlsanity.com/paper/1609.01051/) (2016)
- [GPflowOpt: A Bayesian Optimization Library using TensorFlow](https://mlsanity.com/paper/1711.03845/) (2017)
- [MFES-HB: Efficient Hyperband with Multi-Fidelity Quality Measurements](https://mlsanity.com/paper/2012.03011/) (2020)

## Built on by (most cited first, 30 of 46)

- [SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization](https://mlsanity.com/paper/2109.09831/) (2021)
- [Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges](https://mlsanity.com/paper/2107.05847/) (2021)
- [Graph Attention Multi-Layer Perceptron](https://mlsanity.com/paper/2206.04355/) (2022)
- [Node Dependent Local Smoothing for Scalable Graph Learning](https://mlsanity.com/paper/2110.14377/) (2021)
- [PaSca: a Graph Neural Architecture Search System under the Scalable Paradigm](https://mlsanity.com/paper/2203.00638/) (2022)
- [Multi-Objective Hyperparameter Optimization in Machine Learning -- An Overview](https://mlsanity.com/paper/2206.07438/) (2022)
- [BlindFL: Vertical Federated Machine Learning without Peeking into Your Data](https://mlsanity.com/paper/2206.07975/) (2022)
- [Graph Condensation for Inductive Node Representation Learning](https://mlsanity.com/paper/2307.15967/) (2023)
- [Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence Maximization](https://mlsanity.com/paper/2108.00219/) (2021)
- [Transfer Learning for Bayesian Optimization: A Survey](https://mlsanity.com/paper/2302.05927/) (2023)
- [Model Degradation Hinders Deep Graph Neural Networks](https://mlsanity.com/paper/2206.04361/) (2022)
- [VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition](https://mlsanity.com/paper/2107.08861/) (2021)
- [RIM: Reliable Influence-based Active Learning on Graphs](https://mlsanity.com/paper/2110.14854/) (2021)
- [AutoShot: A Short Video Dataset and State-of-the-Art Shot Boundary Detection](https://mlsanity.com/paper/2304.06116/) (2023)
- [ProxyBO: Accelerating Neural Architecture Search via Bayesian Optimization with Zero-cost Proxies](https://mlsanity.com/paper/2110.10423/) (2021)
- [Evaluating Deep Graph Neural Networks](https://mlsanity.com/paper/2108.00955/) (2021)
- [Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox Optimization](https://mlsanity.com/paper/2207.13676/) (2022)
- [Information Gain Propagation: a new way to Graph Active Learning with Soft Labels](https://mlsanity.com/paper/2203.01093/) (2022)
- [Equal Opportunity of Coverage in Fair Regression](https://mlsanity.com/paper/2311.02243/) (2023)
- [Hyper-Tune: Towards Efficient Hyper-parameter Tuning at Scale](https://mlsanity.com/paper/2201.06834/) (2022)
- [Transfer Learning based Search Space Design for Hyperparameter Tuning](https://mlsanity.com/paper/2206.02511/) (2022)
- [NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation Learning](https://mlsanity.com/paper/2206.08583/) (2022)
- [OpenBox: A Python Toolkit for Generalized Black-box Optimization](https://mlsanity.com/paper/2304.13339/) (2023)
- [TransBO: Hyperparameter Optimization via Two-Phase Transfer Learning](https://mlsanity.com/paper/2206.02663/) (2022)
- [CBLab: Supporting the Training of Large-scale Traffic Control Policies with Scalable Traffic Simulation](https://mlsanity.com/paper/2210.00896/) (2022)
- [DivBO: Diversity-aware CASH for Ensemble Learning](https://mlsanity.com/paper/2302.03255/) (2023)
- [Does Long-Term Series Forecasting Need Complex Attention and Extra Long Inputs?](https://mlsanity.com/paper/2306.05035/) (2023)
- [ZOOMER: Boosting Retrieval on Web-scale Graphs by Regions of Interest](https://mlsanity.com/paper/2203.12596/) (2022)
- [A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning](https://mlsanity.com/paper/2303.05710/) (2023)
- [PSEO: Optimizing Post-hoc Stacking Ensemble Through Hyperparameter Tuning](https://mlsanity.com/paper/2508.05144/) (2025)

## Abstract (arXiv)

Black-box optimization (BBO) has a broad range of applications, including automatic machine learning, engineering, physics, and experimental design. However, it remains a challenge for users to apply BBO methods to their problems at hand with existing software packages, in terms of applicability, performance, and efficiency. In this paper, we build OpenBox, an open-source and general-purpose BBO service with improved usability. The modular design behind OpenBox also facilitates flexible abstraction and optimization of basic BBO components that are common in other existing systems. OpenBox is distributed, fault-tolerant, and scalable. To improve efficiency, OpenBox further utilizes "algorithm agnostic" parallelization and transfer learning. Our experimental results demonstrate the effectiveness and efficiency of OpenBox compared to existing systems.