mlsanityData to 7 Oct 2026

SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization

arXiv 2109.09831 ↗First posted 20 Sep 2021cs.LG stat.MLcited by 229

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Abstract (arXiv)

Algorithm parameters, in particular hyperparameters of machine learning algorithms, can substantially impact their performance. To support users in determining well-performing hyperparameter configurations for their algorithms, datasets and applications at hand, SMAC3 offers a robust and flexible framework for Bayesian Optimization, which can improve performance within a few evaluations. It offers several facades and pre-sets for typical use cases, such as optimizing hyperparameters, solving low dimensional continuous (artificial) global optimization problems and configuring algorithms to perform well across multiple problem instances. The SMAC3 package is available under a permissive BSD-license at https://github.com/automl/SMAC3.

Key facts. Cited by 229 papers in the map (data through 7 Oct 2026). Builds on 20 of its 67 references that are in the map. First posted on arXiv 20 Sep 2021.

Builds on

20 of 67 references.

References · 20

  1. 2016Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimizationcited by 688
  2. 2018BOHB: Robust and Efficient Hyperparameter Optimization at Scalecited by 466
  3. 2019Scalable Global Optimization via Local Bayesian Optimizationcited by 359
  4. 2019Understanding and Robustifying Differentiable Architecture Searchcited by 176
  5. 2021Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020cited by 146
  6. 2021HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPOcited by 111
  7. 2014ParamILS: An Automatic Algorithm Configuration Frameworkcited by 88
  8. 2016Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Sciencecited by 73
  9. 2020Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDLcited by 67
  10. 2019Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonflycited by 66
  11. 2020NAS-Bench-1Shot1: Benchmarking and Dissecting One-shot Neural Architecture Searchcited by 64
  12. 2019Tabular Benchmarks for Joint Architecture and Hyperparameter Optimizationcited by 57
8 more
  1. 2016CMA-ES for Hyperparameter Optimization of Deep Neural Networkscited by 47
  2. 2021OpenBox: A Generalized Black-box Optimization Servicecited by 46
  3. 2018Practical Design Space Explorationcited by 35
  4. 2020Time Efficiency in Optimization with a Bayesian-Evolutionary Algorithmcited by 27
  5. 2019BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameterscited by 25
  6. 2019Towards Assessing the Impact of Bayesian Optimization's Own Hyperparameterscited by 16
  7. 2015The Configurable SAT Solver Challenge (CSSC)cited by 9
  8. 2020Squirrel: A Switching Hyperparameter Optimizercited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 33; 2023: 47; 2024: 55; 2025: 56; 2026: 36.

229 papers.

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  1. 2023Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performancecited by 143
  2. 2023Neural Architecture Search: Insights from 1000 Paperscited by 136
  3. 2024Large Language Models to Enhance Bayesian Optimizationcited by 127
  4. 2022Efficient Methods for Natural Language Processing: A Surveycited by 78
  5. 2023Using Large Language Models for Hyperparameter Optimizationcited by 71
  6. 2022πBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimizationcited by 69
  7. 2023Hyperparameters in Reinforcement Learning and How To Tune Themcited by 58
  8. 2023A Study of Bayesian Neural Network Surrogates for Bayesian Optimizationcited by 50
  9. 2024LLaMoCo: Instruction Tuning of Large Language Models for Optimization Code Generationcited by 45
  10. 2023Will Affective Computing Emerge from Foundation Models and General AI? A First Evaluation on ChatGPTcited by 41
  11. 2022Achieving Risk Control in Online Learning Settingscited by 38
  12. 2022Automated Dynamic Algorithm Configurationcited by 37
38 more
  1. 2023AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Riskscited by 34
  2. 2023A Wide Evaluation of ChatGPT on Affective Computing Taskscited by 30
  3. 2024AutoLoRA: Automatically Tuning Matrix Ranks in Low-Rank Adaptation Based on Meta Learningcited by 30
  4. 2022A Survey of Methods for Automated Algorithm Configurationcited by 29
  5. 2024In-the-loop Hyper-Parameter Optimization for LLM-Based Automated Design of Heuristicscited by 28
  6. 2023SMT 2.0: A Surrogate Modeling Toolbox with a focus on Hierarchical and Mixed Variables Gaussian Processescited by 27
  7. 2022Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognitioncited by 26
  8. 2022Adaptive Cut Selection in Mixed-Integer Linear Programmingcited by 25
  9. 2024Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-Optimizationcited by 23
  10. 2023A General Framework for User-Guided Bayesian Optimizationcited by 22
  11. 2024Symbol: Generating Flexible Black-Box Optimizers through Symbolic Equation Learningcited by 22
  12. 2022Efficiently Controlling Multiple Risks with Pareto Testingcited by 21
  13. 2022Bayesian Generational Population-Based Trainingcited by 20
  14. 2023Machine Learning for Cutting Planes in Integer Programming: A Surveycited by 19
  15. 2022Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox Optimizationcited by 17
  16. 2023Scaling Laws for Hyperparameter Optimizationcited by 16
  17. 2023PED-ANOVA: Efficiently Quantifying Hyperparameter Importance in Arbitrary Subspacescited by 15
  18. 2024ConfigX: Modular Configuration for Evolutionary Algorithms via Multitask Reinforcement Learningcited by 15
  19. 2022Efficient Automated Deep Learning for Time Series Forecastingcited by 14
  20. 2022DeepCAVE: An Interactive Analysis Tool for Automated Machine Learningcited by 14
  21. 2022Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spacescited by 14
  22. 2024Position: A Call to Action for a Human-Centered AutoML Paradigmcited by 14
  23. 2025OptunaHub: A Platform for Black-Box Optimizationcited by 14
  24. 2022Transfer Learning based Search Space Design for Hyperparameter Tuningcited by 13
  25. 2024Adaptive Learn-then-Test: Statistically Valid and Efficient Hyperparameter Selectioncited by 13
  26. 2023Automated Design of Metaheuristic Algorithms: A Surveycited by 12
  27. 2022BaCO: A Fast and Portable Bayesian Compiler Optimization Frameworkcited by 11
  28. 2024Artificial Intelligence for Operations Research: Revolutionizing the Operations Research Processcited by 11
  29. 2024LLM-Enhanced Bayesian Optimization for Efficient Analog Layout Constraint Generationcited by 11
  30. 2024Explainable Benchmarking for Iterative Optimization Heuristicscited by 10
  31. 2025LLaMEA-BO: A Large Language Model Evolutionary Algorithm for Automatically Generating Bayesian Optimization Algorithmscited by 10
  32. 2022c-TPE: Tree-structured Parzen Estimator with Inequality Constraints for Expensive Hyperparameter Optimizationcited by 9
  33. 2023OpenBox: A Python Toolkit for Generalized Black-box Optimizationcited by 9
  34. 2024Deep-ELA: Deep Exploratory Landscape Analysis with Self-Supervised Pretrained Transformers for Single- and Multi-Objective Continuous Optimization Problemscited by 9
  35. 2024E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Modelcited by 9
  36. 2024Hyperparameter Importance Analysis for Multi-Objective AutoMLcited by 9
  37. 2025BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RLcited by 9
  38. 2023Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learningcited by 8

50 most cited of 229 All 229 (JSON)