mlsanityData to 7 Oct 2026

BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters

arXiv 1908.06756 ↗First posted 16 Aug 2019cs.LG cs.AI stat.MLcited by 25

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Abstract (arXiv)

Hyperparameter optimization and neural architecture search can become prohibitively expensive for regular black-box Bayesian optimization because the training and evaluation of a single model can easily take several hours. To overcome this, we introduce a comprehensive tool suite for effective multi-fidelity Bayesian optimization and the analysis of its runs. The suite, written in Python, provides a simple way to specify complex design spaces, a robust and efficient combination of Bayesian optimization and HyperBand, and a comprehensive analysis of the optimization process and its outcomes.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Aug 2019.

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Citing papers per year: 2021: 6; 2022: 5; 2023: 3; 2024: 6; 2025: 4; 2026: 1.

25 papers.

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  1. 2021SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimizationcited by 229
  2. 2021YAHPO Gym -- An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimizationcited by 61
  3. 2024In-the-loop Hyper-Parameter Optimization for LLM-Based Automated Design of Heuristicscited by 28
  4. 2021CATE: Computation-aware Neural Architecture Encoding with Transformerscited by 21
  5. 2022Multi-Fidelity Cost-Aware Bayesian Optimizationcited by 20
  6. 2023ytopt: Autotuning Scientific Applications for Energy Efficiency at Large Scalescited by 13
  7. 2022Reinforcement Learning with Automated Auxiliary Loss Searchcited by 12
  8. 2022BaCO: A Fast and Portable Bayesian Compiler Optimization Frameworkcited by 11
  9. 2024Explainable Benchmarking for Iterative Optimization Heuristicscited by 10
  10. 2021Naive Automated Machine Learningcited by 8
  11. 2023Machine Learning-Assisted Discovery of Flow Reactor Designscited by 8
  12. 2024X Hacking: The Threat of Misguided AutoMLcited by 5
13 more
  1. 2021Mutation is all you needcited by 3
  2. 2025carps: A Framework for Comparing N Hyperparameter Optimizers on M Benchmarkscited by 3
  3. 2025Modeling Hierarchical Spaces: A Review and Unified Framework for Surrogate-Based Architecture Designcited by 3
  4. 2022Deep Gaussian Process-based Multi-fidelity Bayesian Optimization for Simulated Chemical Reactorscited by 2
  5. 2023Autotuning Apache TVM-based Scientific Applications Using Bayesian Optimizationcited by 2
  6. 2025Enhancing Explainability and Reliable Decision-Making in Particle Swarm Optimization through Communication Topologiescited by 2
  7. 2021Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization (extended version)cited by 1
  8. 2022Multi-objective hyperparameter optimization with performance uncertaintycited by 0
  9. 2024Efficient Automatic Tuning for Data-driven Model Predictive Control via Meta-Learningcited by 0
  10. 2024Accel-NASBench: Sustainable Benchmarking for Accelerator-Aware NAScited by 0
  11. 2024An Autotuning-based Optimization Framework for Mixed-kernel SVM Classifications in Smart Pixel Datasets and Heterojunction Transistorscited by 0
  12. 2025Explainable Information Processing in Particle Swarm Optimization through Landscape and Search Behavior Analysiscited by 0
  13. 2026Conditional PED-ANOVA: Hyperparameter Importance in Hierarchical & Dynamic Search Spacescited by 0