OptunaAutoML

Optuna is an open source hyperparameter optimization framework to automate hyperparameter search

[OptunaAutoML @OptunaAutoML]

Optuna v4.6 has been released!
📊Optuna Dashboard LLM Integration
🚀Significant speedup of GPSampler
🦾Full support for multi-objective and constrained optimization in AutoSampler
⛏️Robust Bayesian Optimization Algorithms
https://t.co/7Z4ghVTbK9

[OptunaAutoML @OptunaAutoML]

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:
https://t.co/wBoa2Bw7hB

[OptunaAutoML @OptunaAutoML]

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: https://t.co/thPAVLfwra

[OptunaAutoML @OptunaAutoML]

Optuna can also be applied in material science. A blog post has been published detailing its use case in crystal structure prediction.
https://t.co/mcIGqTf0Fe

[OptunaAutoML @OptunaAutoML]

Optuna v4.8.0 has been released! 🚀

This release introduces the constant liar strategy to GPSampler for improved distributed optimization (currently for single-objective & unconstrained tasks). Further improvements are coming in v4.9.0!

👇See what’s new
https://t.co/uH5M7uUmVi

[OptunaAutoML @OptunaAutoML]

Optuna v4.7.0 has been released! This is a maintenance release with various minor bug fixes and improvements to the documentation and more.

👇See what’s new
https://t.co/5syvxZEPfv

[OptunaAutoML @OptunaAutoML]

Nice work! This research uses Optuna for multi-objective black-box optimization in manipulator design.

[Kento Kawaharazuka / 河原塚 健人 @KKawaharazuka]

🚀Our New Paper "Analysis of Various Manipulator Configurations Based on Multi-Objective Black-Box Optimization" is now published in Advanced Robotics!

We compare manipulator configurations via multi-objective optimization and explore future designs!

https://t.co/bt9piicau4

[Takuya Akiba @iwiwi]

Honored to be interviewed about the history of Optuna. It covers everything from our early challenges in 2018 to the mindset I carry into my work today.

Full interview (in Japanese): https://t.co/bz5E0G9xxU

[OptunaAutoML @OptunaAutoML]

Optuna Dashboard now supports LLM integration🎉

You can now ask in natural language to generate Plotly charts or filter trials. Learn how we designed and implemented these LLM features.
https://t.co/zcdmf9sdVF

[OptunaAutoML @OptunaAutoML]

AutoSampler now fully supports multi-objective & constrained optimization! AutoSampler lets you efficiently solve a wide range of problems without worrying about which optimization algorithm to use. Learn more in our article: https://t.co/Luvlvp3hyJ

[Takuya Akiba @iwiwi]

Our Optuna paper has reached over 10,000 citations! A huge thank you to the amazing co-authors, contributors, and community! 🌀🚀

[OptunaAutoML @OptunaAutoML]

We published an arXiv preprint entitled "OptunaHub: A Platform for Black-Box Optimization." The paper describes the motivation and the ecosystem overview.
https://t.co/SuQQOep9aF

[OptunaAutoML @OptunaAutoML]

Kaito Baba (@kAIto47802) added constraint handling for multi-objective optimization by GPSampler, a Gaussian process-based Bayesian optimization. It is especially beneficial when many expensive metrics need to be considered. Learn more in our article: https://t.co/Y7EiJEfIyK

[OptunaAutoML @OptunaAutoML]

We recently published an arXiv article about a sample-efficient black-box combinatorial optimization for TPE by considering distance structures of categorical parameters.
This feature is available via categorical_distance_func in TPESampler.
https://t.co/r6JGtCJTTb

[OptunaAutoML @OptunaAutoML]

Optuna v4.5 has been released!

⛏️GPSampler for constrained multi-objective optimization
🚀Significant speedup of TPESampler and plot_hypervolume_history
🦾CmaEsSampler now supports 1D search space
🐍The optunahub library is published on conda-forge

https://t.co/QS314M0DaI

[OptunaAutoML @OptunaAutoML]

Optuna v4.4 is now available as of June 16! This release introduces the Optuna MCP Server, our first LLM-based toolchain. We're already hard at work on Optuna v5, following our roadmap. For full details, check out our blog post.

https://t.co/TJHPWcBhgB

[NumFOCUS @NumFOCUS]

Optuna v4.4 will be released this month, and the roadmap for their next exciting major release- Optuna v5- has just been published! Read more on their blog here: https://t.co/7RYfb1OZPT

[OptunaAutoML @OptunaAutoML]

See the release blog for more details. https://t.co/TJHPWcBhgB

[OptunaAutoML @OptunaAutoML]

Optuna v4.4 has been released with various new features, bug fixes, and enhancements.
🚀Optuna MCP server, which is our first LLM intensive toolchain
✅Gaussian process-based algorithm now supports multi-objective optimization
🌀A lot of new features in OptunaHub

[OptunaAutoML @OptunaAutoML]

Check out the release note! https://t.co/MgyEbWQSuQ

[OptunaAutoML @OptunaAutoML]

See the repository for more details of Optuna MCP server https://t.co/MvUCXPwjzW