# OptunaHub: A Platform for Black-Box Optimization

arXiv: https://arxiv.org/abs/2510.02798 · HTML: https://mlsanity.com/paper/2510.02798/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 33 references that are in the map.
- First posted on arXiv 3 Oct 2025.

## Builds on (most cited first, 22 of 22)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search](https://mlsanity.com/paper/2001.00326/) (2020)
- [SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization](https://mlsanity.com/paper/2109.09831/) (2021)
- [pymoo: Multi-objective Optimization in Python](https://mlsanity.com/paper/2002.04504/) (2020)
- [Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020](https://mlsanity.com/paper/2104.10201/) (2021)
- [Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance](https://mlsanity.com/paper/2304.11127/) (2023)
- [COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting](https://mlsanity.com/paper/1603.08785/) (2016)
- [Large Language Models to Enhance Bayesian Optimization](https://mlsanity.com/paper/2402.03921/) (2024)
- [HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO](https://mlsanity.com/paper/2109.06716/) (2021)
- [NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size](https://mlsanity.com/paper/2009.00437/) (2020)
- [HEBO Pushing The Limits of Sample-Efficient Hyperparameter Optimisation](https://mlsanity.com/paper/2012.03826/) (2020)
- [PFNs4BO: In-Context Learning for Bayesian Optimization](https://mlsanity.com/paper/2305.17535/) (2023)
- [YAHPO Gym -- An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimization](https://mlsanity.com/paper/2109.03670/) (2021)
- [Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization](https://mlsanity.com/paper/1905.04970/) (2019)
- [PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization](https://mlsanity.com/paper/1701.00879/) (2017)
- [IOHprofiler: A Benchmarking and Profiling Tool for Iterative Optimization Heuristics](https://mlsanity.com/paper/1810.05281/) (2018)
- [Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox Optimization](https://mlsanity.com/paper/2207.13676/) (2022)
- [The AI Community Building the Future? A Quantitative Analysis of Development Activity on Hugging Face Hub](https://mlsanity.com/paper/2405.13058/) (2024)
- [OpenBox: A Python Toolkit for Generalized Black-box Optimization](https://mlsanity.com/paper/2304.13339/) (2023)
- [Large-scale Benchmarking of Metaphor-based Optimization Heuristics](https://mlsanity.com/paper/2402.09800/) (2024)
- [Single and Multi-Objective Optimization Benchmark Problems Focusing on Human-Powered Aircraft Design](https://mlsanity.com/paper/2312.08953/) (2023)
- [CatCMA : Stochastic Optimization for Mixed-Category Problems](https://mlsanity.com/paper/2405.09962/) (2024)

## Built on by (most cited first, 14 of 14)

- [Can LLMs Beat Classical Hyperparameter Optimization Algorithms? A Study on autoresearch](https://mlsanity.com/paper/2603.24647/) (2026)
- [SemanticOpt: Towards LLM-Based Semantic Black-Box Optimization](https://mlsanity.com/paper/2510.25404/) (2025)
- [Towards plausibility in time series counterfactual explanations](https://mlsanity.com/paper/2603.08349/) (2026)
- [Beyond the Laplacian: Doubly Stochastic Matrices for Graph Neural Networks](https://mlsanity.com/paper/2604.15069/) (2026)
- [A Probabilistic Consensus-Driven Approach for Robust Counterfactual Explanations](https://mlsanity.com/paper/2604.17494/) (2026)
- [Financially Guided Deep Portfolio Optimization](https://mlsanity.com/paper/2605.28853/) (2026)
- [Optuna Constrained Tree-Structured Parzen Estimator Is a Joint Density Generalization of c-TPE](https://mlsanity.com/paper/2606.09889/) (2026)
- [SpeechDx: A Multi-Task Benchmark for Clinical Speech AI](https://mlsanity.com/paper/2606.17339/) (2026)
- [Bayesian Complete-Pooling in Cross-Subject Classification for Motor Imagery Electroencephalogram](https://mlsanity.com/paper/2607.22980/) (2026)
- [BBOWP-Bench: Evaluating LLMs on Black-Box Optimization Word Problems](https://mlsanity.com/paper/2608.02612/) (2026)
- [Beyond the Gegenbauer Paradigm: q-Orthogonal Kernels for Machine Learning](https://mlsanity.com/paper/2608.03482/) (2026)
- [A Hybrid Framework of Vision Transformer and Gated Recurrent Unit for Detection of Mosquito Diseases](https://mlsanity.com/paper/2608.11582/) (2026)
- [Abliteration Mitigation via Refusal Aliases](https://mlsanity.com/paper/2608.18093/) (2026)
- [AURA: Angular Update Rate Adaptation for training complex-valued neural networks](https://mlsanity.com/paper/2609.25914/) (2026)

## Abstract (arXiv)

Black-box optimization (BBO) underpins advances in domains such as AutoML and Materials Informatics, yet implementations of algorithms and benchmarks remain fragmented across research communities. We introduce OptunaHub (https://hub.optuna.org/), a community-oriented, decentralized platform for distributing BBO components under a unified Optuna-compatible interface. OptunaHub enables independent publication, discovery, and reuse of optimization algorithms and benchmark problems through a lightweight Python module, a contributor-driven registry, and a searchable web interface. The source code is publicly available in the hrefhttps://github.com/optuna/optunahubtextttoptunahub, hrefhttps://github.com/optuna/optunahub-registrytextttoptunahub-registry, and hrefhttps://github.com/optuna/optunahub-webtextttoptunahub-web repositories under the Optuna organization on GitHub (https://github.com/optuna/).