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

arXiv: https://arxiv.org/abs/1908.06756 · HTML: https://mlsanity.com/paper/1908.06756/

## Key facts

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

## Built on by (most cited first, 25 of 25)

- [SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization](https://mlsanity.com/paper/2109.09831/) (2021)
- [YAHPO Gym -- An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimization](https://mlsanity.com/paper/2109.03670/) (2021)
- [In-the-loop Hyper-Parameter Optimization for LLM-Based Automated Design of Heuristics](https://mlsanity.com/paper/2410.16309/) (2024)
- [CATE: Computation-aware Neural Architecture Encoding with Transformers](https://mlsanity.com/paper/2102.07108/) (2021)
- [Multi-Fidelity Cost-Aware Bayesian Optimization](https://mlsanity.com/paper/2211.02732/) (2022)
- [ytopt: Autotuning Scientific Applications for Energy Efficiency at Large Scales](https://mlsanity.com/paper/2303.16245/) (2023)
- [Reinforcement Learning with Automated Auxiliary Loss Search](https://mlsanity.com/paper/2210.06041/) (2022)
- [BaCO: A Fast and Portable Bayesian Compiler Optimization Framework](https://mlsanity.com/paper/2212.11142/) (2022)
- [Explainable Benchmarking for Iterative Optimization Heuristics](https://mlsanity.com/paper/2401.17842/) (2024)
- [Naive Automated Machine Learning](https://mlsanity.com/paper/2111.14514/) (2021)
- [Machine Learning-Assisted Discovery of Flow Reactor Designs](https://mlsanity.com/paper/2308.08841/) (2023)
- [X Hacking: The Threat of Misguided AutoML](https://mlsanity.com/paper/2401.08513/) (2024)
- [Mutation is all you need](https://mlsanity.com/paper/2107.07343/) (2021)
- [carps: A Framework for Comparing N Hyperparameter Optimizers on M Benchmarks](https://mlsanity.com/paper/2506.06143/) (2025)
- [Modeling Hierarchical Spaces: A Review and Unified Framework for Surrogate-Based Architecture Design](https://mlsanity.com/paper/2506.22621/) (2025)
- [Deep Gaussian Process-based Multi-fidelity Bayesian Optimization for Simulated Chemical Reactors](https://mlsanity.com/paper/2210.17213/) (2022)
- [Autotuning Apache TVM-based Scientific Applications Using Bayesian Optimization](https://mlsanity.com/paper/2309.07235/) (2023)
- [Enhancing Explainability and Reliable Decision-Making in Particle Swarm Optimization through Communication Topologies](https://mlsanity.com/paper/2504.12803/) (2025)
- [Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization (extended version)](https://mlsanity.com/paper/2104.13242/) (2021)
- [Multi-objective hyperparameter optimization with performance uncertainty](https://mlsanity.com/paper/2209.04340/) (2022)
- [Efficient Automatic Tuning for Data-driven Model Predictive Control via Meta-Learning](https://mlsanity.com/paper/2404.00232/) (2024)
- [Accel-NASBench: Sustainable Benchmarking for Accelerator-Aware NAS](https://mlsanity.com/paper/2404.08005/) (2024)
- [An Autotuning-based Optimization Framework for Mixed-kernel SVM Classifications in Smart Pixel Datasets and Heterojunction Transistors](https://mlsanity.com/paper/2406.18445/) (2024)
- [Explainable Information Processing in Particle Swarm Optimization through Landscape and Search Behavior Analysis](https://mlsanity.com/paper/2509.06272/) (2025)
- [Conditional PED-ANOVA: Hyperparameter Importance in Hierarchical & Dynamic Search Spaces](https://mlsanity.com/paper/2601.20800/) (2026)

## 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.