Pinned
Release candidate of Optuna v5 is available! Update and report bugs for the next major version.
📈Massive default sampler algorithm enhancement
🔨Conditional PED-ANOVA (KDD 2026)
🌈New API for constrained optimization
OptunaAutoML
@OptunaAutoML
Optuna is an open source hyperparameter optimization framework to automate hyperparameter search
Joined February 2020
- We’ll be presenting our work, “Conditional PED-ANOVA: Hyperparameter Importance in Hierarchical & Dynamic Search Spaces”, at #KDD2026 🇰🇷 The method will be the default hyperparameter importance evaluator in Optuna v5 🎉 Stop by to learn more! dl.acm.org/doi/10.1145/37…
- Optuna Dashboard JupyterLab Extension v0.3.0 is out. This release significantly reduces dependencies, resulting in lower supply-chain risk, a ~50% smaller JS bundle, and lower browser memory usage. Release notes:
- Optuna v4.9.0 has been released! 🚀 This release enhances GPSampler with the Kriging Believer strategy, significantly improving parallel efficiency for multi-objective and constrained optimization. 👇 Check out the full release notes:
- Eri Sawada introduces the constant-liar and Kriging-believer approaches to Optuna's GPSampler. These methods enable efficient parallelization for single-objective, multi-objective, and constrained optimization. Learn more in our article:
