OptunaHub: A Platform for Black-Box Optimization
Authors
Research Topics
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
AuthorShuhei Watanabe
AuthorToshihiko Yanase
AuthorResearch Topics & Keywords
Computational Statistics
Research AreaCitation 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 }
}