mlsanityData to 7 Oct 2026

OpenBox: A Generalized Black-box Optimization Service

arXiv 2106.00421 ↗First posted 1 Jun 2021cs.LG cs.AIcited by 46

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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.

Key facts. Cited by 46 papers in the map (data through 7 Oct 2026). Builds on 18 of its 51 references that are in the map. First posted on arXiv 1 Jun 2021.

Builds on

18 of 51 references.

References · 18

  1. 2016Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimizationcited by 688
  2. 2018BOHB: Robust and Efficient Hyperparameter Optimization at Scalecited by 466
  3. 2020Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimizationcited by 176
  4. 2019Variational Bayesian Optimal Experimental Designcited by 116
  5. 2015Batch Bayesian Optimization via Local Penalizationcited by 103
  6. 2018A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizationscited by 98
  7. 2020Scalable Constrained Bayesian Optimizationcited by 84
  8. 2016Multi-Information Source Optimizationcited by 83
  9. 2020Max-value Entropy Search for Multi-Objective Bayesian Optimization with Constraintscited by 81
  10. 2014Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictionscited by 66
  11. 2015Predictive Entropy Search for Bayesian Optimization with Unknown Constraintscited by 51
  12. 2015A General Framework for Constrained Bayesian Optimization using Information-based Searchcited by 50
6 more
  1. 2020Fair Bayesian Optimizationcited by 45
  2. 2020Efficient Automatic CASH via Rising Banditscited by 30
  3. 2016Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangiancited by 29
  4. 2016Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraintscited by 27
  5. 2017GPflowOpt: A Bayesian Optimization Library using TensorFlowcited by 18
  6. 2020MFES-HB: Efficient Hyperband with Multi-Fidelity Quality Measurementscited by 18

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Citing papers per year: 2021: 12; 2022: 15; 2023: 11; 2024: 3; 2025: 5.

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  1. 2021SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimizationcited by 229
  2. 2021Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challengescited by 143
  3. 2022Graph Attention Multi-Layer Perceptroncited by 82
  4. 2021Node Dependent Local Smoothing for Scalable Graph Learningcited by 45
  5. 2022PaSca: a Graph Neural Architecture Search System under the Scalable Paradigmcited by 37
  6. 2022Multi-Objective Hyperparameter Optimization in Machine Learning -- An Overviewcited by 37
  7. 2022BlindFL: Vertical Federated Machine Learning without Peeking into Your Datacited by 36
  8. 2023Graph Condensation for Inductive Node Representation Learningcited by 31
  9. 2021Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence Maximizationcited by 28
  10. 2023Transfer Learning for Bayesian Optimization: A Surveycited by 27
  11. 2022Model Degradation Hinders Deep Graph Neural Networkscited by 26
  12. 2021VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decompositioncited by 25
34 more
  1. 2021RIM: Reliable Influence-based Active Learning on Graphscited by 23
  2. 2023AutoShot: A Short Video Dataset and State-of-the-Art Shot Boundary Detectioncited by 22
  3. 2021ProxyBO: Accelerating Neural Architecture Search via Bayesian Optimization with Zero-cost Proxiescited by 20
  4. 2021Evaluating Deep Graph Neural Networkscited by 19
  5. 2022Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox Optimizationcited by 17
  6. 2022Information Gain Propagation: a new way to Graph Active Learning with Soft Labelscited by 16
  7. 2023Equal Opportunity of Coverage in Fair Regressioncited by 16
  8. 2022Hyper-Tune: Towards Efficient Hyper-parameter Tuning at Scalecited by 13
  9. 2022Transfer Learning based Search Space Design for Hyperparameter Tuningcited by 13
  10. 2022NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation Learningcited by 11
  11. 2023OpenBox: A Python Toolkit for Generalized Black-box Optimizationcited by 9
  12. 2022TransBO: Hyperparameter Optimization via Two-Phase Transfer Learningcited by 7
  13. 2022CBLab: Supporting the Training of Large-scale Traffic Control Policies with Scalable Traffic Simulationcited by 6
  14. 2023DivBO: Diversity-aware CASH for Ensemble Learningcited by 6
  15. 2023Does Long-Term Series Forecasting Need Complex Attention and Extra Long Inputs?cited by 5
  16. 2022ZOOMER: Boosting Retrieval on Web-scale Graphs by Regions of Interestcited by 4
  17. 2023A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuningcited by 4
  18. 2025PSEO: Optimizing Post-hoc Stacking Ensemble Through Hyperparameter Tuningcited by 4
  19. 2021Bayesian Optimisation for Sequential Experimental Design with Applications in Additive Manufacturingcited by 3
  20. 2021Automated Hyperparameter Optimization Challenge at CIKM 2021 AnalyticCupcited by 3
  21. 2023Rover: An online Spark SQL tuning service via generalized transfer learningcited by 3
  22. 2021SpeechNAS: Towards Better Trade-off between Latency and Accuracy for Large-Scale Speaker Verificationcited by 2
  23. 2022DFG-NAS: Deep and Flexible Graph Neural Architecture Searchcited by 1
  24. 2023Towards General and Efficient Online Tuning for Sparkcited by 1
  25. 2024Budget-aware Query Tuning: An AutoML Perspectivecited by 1
  26. 2021K-Core Decomposition on Super Large Graphs with Limited Resourcescited by 0
  27. 2022Efficient End-to-End AutoML via Scalable Search Space Decompositioncited by 0
  28. 2023Grey-box Bayesian Optimization for Sensor Placement in Assisted Living Environmentscited by 0
  29. 2024General Automatic Solution Generation of Social Problemscited by 0
  30. 2024Regularized boosting with an increasing coefficient magnitude stop criterion as meta-learner in hyperparameter optimization stacking ensemblecited by 0
  31. 2025Improving Bayesian Optimization for Portfolio Management with an Adaptive Schedulingcited by 0
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  33. 2025Centrum: Model-based Database Auto-tuning with Minimal Distributional Assumptionscited by 0
  34. 2025Energy-Efficient Autonomous Driving with Adaptive Perception and Robust Decisioncited by 0