JMLR

OptunaHub: A Platform for Black-Box Optimization

Authors
Yoshihiko Ozaki Shuhei Watanabe Toshihiko Yanase
Research Topics
Computational Statistics
Paper Information
  • Journal:
    Journal of Machine Learning Research
  • Added to Tracker:
    Sep 09, 2026
Abstract

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 optunahub, optunahub-registry, and optunahub-web repositories under the Optuna organization on GitHub (https://github.com/optuna/).

Author Details
Yoshihiko Ozaki
Author
Shuhei Watanabe
Author
Toshihiko Yanase
Author
Research Topics & Keywords
Computational Statistics
Research Area
Citation Information
APA Format
Yoshihiko Ozaki , Shuhei Watanabe & Toshihiko Yanase . OptunaHub: A Platform for Black-Box Optimization. Journal of Machine Learning Research .
BibTeX Format
@article{paper1659,
  title = { OptunaHub: A Platform for Black-Box Optimization },
  author = { Yoshihiko Ozaki and Shuhei Watanabe and Toshihiko Yanase },
  journal = { Journal of Machine Learning Research },
  url = { https://www.jmlr.org/papers/v27/25-2424.html }
}