Optuna 4.2.0 for hyperparameter optimization has been released!
- gRPC Storage Proxy for Large-Scale Distributed Optimization
- SMAC3: Support for New State-of-the-Art Optimization Algorithm by AutoML_org
- OptunaHub Supports Benchmarks
- c-TPE and Constrained GPSampler
github.com/optuna/optun...
- gRPC Storage Proxy for Large-Scale Distributed Optimization
- SMAC3: Support for New State-of-the-Art Optimization Algorithm by AutoML_org
- OptunaHub Supports Benchmarks
- c-TPE and Constrained GPSampler
github.com/optuna/optun...
Release v4.2.0 · optuna/optuna
This is the release note of v4.2.0. In conjunction with the Optuna release, OptunaHub 0.2.0 is released. Please refer to the release note of OptunaHub 0.2.0 for more details.
Highlights of this rel...
github.com
January 20, 2025 at 8:06 AM
Optuna 4.2.0 for hyperparameter optimization has been released!
- gRPC Storage Proxy for Large-Scale Distributed Optimization
- SMAC3: Support for New State-of-the-Art Optimization Algorithm by AutoML_org
- OptunaHub Supports Benchmarks
- c-TPE and Constrained GPSampler
github.com/optuna/optun...
- gRPC Storage Proxy for Large-Scale Distributed Optimization
- SMAC3: Support for New State-of-the-Art Optimization Algorithm by AutoML_org
- OptunaHub Supports Benchmarks
- c-TPE and Constrained GPSampler
github.com/optuna/optun...
My #astrocode recommendation of the day is Optuna: a lightweight and very easy to use library for optimizing model hyperparameters. ☄️
I've had a great morning using it for the first time. I'm impressed that it can converge so fast even when optimizing ~10 parameters.
I've had a great morning using it for the first time. I'm impressed that it can converge so fast even when optimizing ~10 parameters.
Optuna - A hyperparameter optimization framework
Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning. It features an imperative, define-by-run style user API.
optuna.org
June 2, 2025 at 9:38 AM
My #astrocode recommendation of the day is Optuna: a lightweight and very easy to use library for optimizing model hyperparameters. ☄️
I've had a great morning using it for the first time. I'm impressed that it can converge so fast even when optimizing ~10 parameters.
I've had a great morning using it for the first time. I'm impressed that it can converge so fast even when optimizing ~10 parameters.
ついにぼくもoptunaデビューしちゃう😳
December 29, 2025 at 12:13 PM
ついにぼくもoptunaデビューしちゃう😳
Did you know that the skrub Data Ops support Optuna as backend to run hyperparameter search?
It's as easy as writing "backend='optuna'": this will set up a default Optuna study (and the TPE sampler) to replace the standard random sampler.
It's as easy as writing "backend='optuna'": this will set up a default Optuna study (and the TPE sampler) to replace the standard random sampler.
February 5, 2026 at 8:52 AM
Did you know that the skrub Data Ops support Optuna as backend to run hyperparameter search?
It's as easy as writing "backend='optuna'": this will set up a default Optuna study (and the TPE sampler) to replace the standard random sampler.
It's as easy as writing "backend='optuna'": this will set up a default Optuna study (and the TPE sampler) to replace the standard random sampler.
I know how many of you learn on Udemy, so I built a new series for you. One skill per course, no filler.
Friday's pick: automating hyperparameter tuning with Optuna.
Start here 👇
https://linktr.ee/solegalli
Friday's pick: automating hyperparameter tuning with Optuna.
Start here 👇
https://linktr.ee/solegalli
September 25, 2026 at 2:04 PM
I know how many of you learn on Udemy, so I built a new series for you. One skill per course, no filler.
Friday's pick: automating hyperparameter tuning with Optuna.
Start here 👇
https://linktr.ee/solegalli
Friday's pick: automating hyperparameter tuning with Optuna.
Start here 👇
https://linktr.ee/solegalli
Conformal Optimization Beats Bayesian Optimization, Optuna and Random Search on 72 classification Datasets
https://thierrymoudiki.github.io/blog/2026/04/19/python/conformalBO-vs-GPBO
#Python #DataScience #MachineLearning #rstats #Techtonique
https://thierrymoudiki.github.io/blog/2026/04/19/python/conformalBO-vs-GPBO
#Python #DataScience #MachineLearning #rstats #Techtonique
September 21, 2026 at 2:43 PM
Conformal Optimization Beats Bayesian Optimization, Optuna and Random Search on 72 classification Datasets
https://thierrymoudiki.github.io/blog/2026/04/19/python/conformalBO-vs-GPBO
#Python #DataScience #MachineLearning #rstats #Techtonique
https://thierrymoudiki.github.io/blog/2026/04/19/python/conformalBO-vs-GPBO
#Python #DataScience #MachineLearning #rstats #Techtonique
Starting to suspect that if we could clone 10,000 of me and get Optuna to run a hyperparameter sweep on my brittle manual solutions they would perform just as well as this model.
July 7, 2025 at 10:34 PM
Starting to suspect that if we could clone 10,000 of me and get Optuna to run a hyperparameter sweep on my brittle manual solutions they would perform just as well as this model.
this 🤏 close to wrapping the whole thing in Optuna
finding myself with a model that has way too many knobs (even the dataset has parameters !!)
long experiments + curse of dimensionality = abject misery
long experiments + curse of dimensionality = abject misery
February 23, 2026 at 7:26 PM
this 🤏 close to wrapping the whole thing in Optuna
Will be going live in a bit to talk about Optuna. Feel free to join live at lunch if you have any questions you'd like me to answer live.
Optuna: a hyperparameter optimization framework
Scikit-learn allows you to perform hyperparameter search but a lot of it happens in memory. Sometimes you want to have a storage layer for these hyperparamet...
buff.ly
November 15, 2024 at 9:21 AM
Will be going live in a bit to talk about Optuna. Feel free to join live at lunch if you have any questions you'd like me to answer live.
競馬は運じゃなく、再現性。
そのために独自AI 《Ultimate Complete》 を開発してます。
🧠 Model Overview
・Optuna × 遺伝的アルゴリズム
・XGBoost / Stacking / SHAP可視化
・調教・馬場・展開を統合した40+ファクター
・荒れるレース識別・回収率フィルタ搭載
🎯目標は“当てる”じゃなく
勝ち筋を再現し続けること。
📌 実績
・三連単5196倍
・年間回収率チャレンジ継続
・中央／地方／海外対応
📍予想・検証・開発ログ
🔗 note.com/koko555513
そのために独自AI 《Ultimate Complete》 を開発してます。
🧠 Model Overview
・Optuna × 遺伝的アルゴリズム
・XGBoost / Stacking / SHAP可視化
・調教・馬場・展開を統合した40+ファクター
・荒れるレース識別・回収率フィルタ搭載
🎯目標は“当てる”じゃなく
勝ち筋を再現し続けること。
📌 実績
・三連単5196倍
・年間回収率チャレンジ継続
・中央／地方／海外対応
📍予想・検証・開発ログ
🔗 note.com/koko555513
November 21, 2025 at 4:12 AM
競馬は運じゃなく、再現性。
そのために独自AI 《Ultimate Complete》 を開発してます。
🧠 Model Overview
・Optuna × 遺伝的アルゴリズム
・XGBoost / Stacking / SHAP可視化
・調教・馬場・展開を統合した40+ファクター
・荒れるレース識別・回収率フィルタ搭載
🎯目標は“当てる”じゃなく
勝ち筋を再現し続けること。
📌 実績
・三連単5196倍
・年間回収率チャレンジ継続
・中央／地方／海外対応
📍予想・検証・開発ログ
🔗 note.com/koko555513
そのために独自AI 《Ultimate Complete》 を開発してます。
🧠 Model Overview
・Optuna × 遺伝的アルゴリズム
・XGBoost / Stacking / SHAP可視化
・調教・馬場・展開を統合した40+ファクター
・荒れるレース識別・回収率フィルタ搭載
🎯目標は“当てる”じゃなく
勝ち筋を再現し続けること。
📌 実績
・三連単5196倍
・年間回収率チャレンジ継続
・中央／地方／海外対応
📍予想・検証・開発ログ
🔗 note.com/koko555513
#ばばさん通信ダイジェスト 賛否関わらず話題になった/なりそうなものを共有しています。
Rustによる高速なOptuna実装の試作
https://tech.preferred.jp/ja/blog/prototyping-rust-optuna/
Rustによる高速なOptuna実装の試作
https://tech.preferred.jp/ja/blog/prototyping-rust-optuna/
Rustによる高速なOptuna実装の試作
はじめに
tech.preferred.jp
April 3, 2024 at 10:55 AM
#ばばさん通信ダイジェスト 賛否関わらず話題になった/なりそうなものを共有しています。
Rustによる高速なOptuna実装の試作
https://tech.preferred.jp/ja/blog/prototyping-rust-optuna/
Rustによる高速なOptuna実装の試作
https://tech.preferred.jp/ja/blog/prototyping-rust-optuna/
optunaの理論 - tomtom58’s blog
https://tomtom58.hatenablog.com/entry/2024/12/15/115922
https://tomtom58.hatenablog.com/entry/2024/12/15/115922
optunaの理論 - tomtom58’s blog
はじめに 従来のフレームワークにおける課題 Optunaの設計思想 optunaの理論 Define-by-run APIの理論と実装 サンプリングアルゴリズムの理論 効率的な枝刈り（Pruning）メカニズム 分散最適化の理論とアーキテクチャ ストレージバックエンド トライアルの同期と非同期実行 実際のユースケースと性能評価 ベンチマーク評価の理論的枠組み TPEとCMA-ESの組み合わせによる性能向上 実世界での応用例 システムの拡張性と実装の詳細 カスタムサンプラーの実装 カスタム枝刈り手法の実装 実装上の最適化とパフォーマンスチューニング データベースアクセスの最適化 メモリ使用量の最…
tomtom58.hatenablog.com
December 28, 2024 at 7:01 AM
optunaの理論 - tomtom58’s blog
https://tomtom58.hatenablog.com/entry/2024/12/15/115922
https://tomtom58.hatenablog.com/entry/2024/12/15/115922
やべーパラメータ算出にグリッドサーチつかっちゃって
永遠に終わらない。optunaにしておけばよかったか
永遠に終わらない。optunaにしておけばよかったか
January 12, 2025 at 1:25 AM
やべーパラメータ算出にグリッドサーチつかっちゃって
永遠に終わらない。optunaにしておけばよかったか
永遠に終わらない。optunaにしておけばよかったか
Announcing Optuna 4.4 We have released version 4.4 of the black-box optimization framework Optuna. We encourage you to check out the release notes! Continue reading on Optuna »
#optuna #mcp-server #automl #llm #technology
Origin | Interest | Match
#optuna #mcp-server #automl #llm #technology
Origin | Interest | Match
Announcing Optuna 4.4
We have released version 4.4 of the black-box optimization framework Optuna. We encourage you to check out the release notes!
medium.com
June 16, 2025 at 5:32 AM
Announcing Optuna 4.4 We have released version 4.4 of the black-box optimization framework Optuna. We encourage you to check out the release notes! Continue reading on Optuna »
#optuna #mcp-server #automl #llm #technology
Origin | Interest | Match
#optuna #mcp-server #automl #llm #technology
Origin | Interest | Match
Skrub 0.7.0 is here! 🎉
✨ Main highlights:
- Tune hyperparameter choices with Optuna
- Added support for Pandas 3.0
- Estimators in data ops can now take additional kwargs
16 new contributors helped with this release 👥
Check out the full changelog: github.com/skrub-data/s...
✨ Main highlights:
- Tune hyperparameter choices with Optuna
- Added support for Pandas 3.0
- Estimators in data ops can now take additional kwargs
16 new contributors helped with this release 👥
Check out the full changelog: github.com/skrub-data/s...
Release Skrub release 0.7.0 · skrub-data/skrub
Release 0.7.0
✨ Highlights
Data Ops can now be tuned with Optuna.
It is now possible to pass extra named arguments to an estimator through DataOps.skb.apply.
The TableReport now supports numpy arr...
github.com
December 12, 2025 at 9:55 AM
Skrub 0.7.0 is here! 🎉
✨ Main highlights:
- Tune hyperparameter choices with Optuna
- Added support for Pandas 3.0
- Estimators in data ops can now take additional kwargs
16 new contributors helped with this release 👥
Check out the full changelog: github.com/skrub-data/s...
✨ Main highlights:
- Tune hyperparameter choices with Optuna
- Added support for Pandas 3.0
- Estimators in data ops can now take additional kwargs
16 new contributors helped with this release 👥
Check out the full changelog: github.com/skrub-data/s...
2/4💡
KI-Experte, sondern Softwareentwickler, und sehr gut Algorithmen entwickeln und testen kann. Aber jetzt wird's konkret.
ChatGPT schlägt vor:
Mesa oder SimPy (je nachdem ob Agenten- oder Prozesslogik dominiert) + Optuna (Parameter-Räume) + SALib (Sensitivität) + MLflow (Tracking).
↩️
KI-Experte, sondern Softwareentwickler, und sehr gut Algorithmen entwickeln und testen kann. Aber jetzt wird's konkret.
ChatGPT schlägt vor:
Mesa oder SimPy (je nachdem ob Agenten- oder Prozesslogik dominiert) + Optuna (Parameter-Räume) + SALib (Sensitivität) + MLflow (Tracking).
↩️
December 31, 2025 at 12:42 PM
2/4💡
KI-Experte, sondern Softwareentwickler, und sehr gut Algorithmen entwickeln und testen kann. Aber jetzt wird's konkret.
ChatGPT schlägt vor:
Mesa oder SimPy (je nachdem ob Agenten- oder Prozesslogik dominiert) + Optuna (Parameter-Räume) + SALib (Sensitivität) + MLflow (Tracking).
↩️
KI-Experte, sondern Softwareentwickler, und sehr gut Algorithmen entwickeln und testen kann. Aber jetzt wird's konkret.
ChatGPT schlägt vor:
Mesa oder SimPy (je nachdem ob Agenten- oder Prozesslogik dominiert) + Optuna (Parameter-Räume) + SALib (Sensitivität) + MLflow (Tracking).
↩️
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: medium.com/optuna/optun...
June 19, 2025 at 7:04 PM
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: medium.com/optuna/optun...
Optuna has released version 4.4 of the black-box optimization framework Optuna. It includes feature additions, bug fixes, improvements in documentation and testing, and their first LLM-intensive toolchain! Check out their blog for more details: medium.com/optuna/annou...
July 21, 2025 at 3:08 PM
Optuna has released version 4.4 of the black-box optimization framework Optuna. It includes feature additions, bug fixes, improvements in documentation and testing, and their first LLM-intensive toolchain! Check out their blog for more details: medium.com/optuna/annou...
Rustによる高速かつ省メモリなOptuna実装「Rustuna」をリリース - 株式会社Preferred Networks
https://www.preferred.jp/ja/news/pr20260907
https://www.preferred.jp/ja/news/pr20260907
Rustによる高速かつ省メモリなOptuna実装「Rustuna」をリリース - 株式会社Preferred Networks
株式会社Preferred Networks（本社：東京都千代田区、代表取締役社長：岡野原 大輔、以下、PFN）は、PFNが2018年から開発を主導してきたオープンソースのハイパーパラメータ自動最適化フレームワークOptuna™（オプチュナ）を、安全性や実行速度が特長のプログラミング言語Rustで実装したRustuna
www.preferred.jp
September 8, 2026 at 8:11 AM
Rustによる高速かつ省メモリなOptuna実装「Rustuna」をリリース - 株式会社Preferred Networks
https://www.preferred.jp/ja/news/pr20260907
https://www.preferred.jp/ja/news/pr20260907
New blog post: Automatic Hyperparameter Tuning in Practice.
Part I was about the challenges and main components of hyperparameter tuning (samplers, …). Part II is about the practical application of this technique to RL with the @optuna.bsky.social and SB3 libraries.
araffin.github.io/post/optuna/
Part I was about the challenges and main components of hyperparameter tuning (samplers, …). Part II is about the practical application of this technique to RL with the @optuna.bsky.social and SB3 libraries.
araffin.github.io/post/optuna/
Automatic Hyperparameter Tuning - In Practice (Part 2) | Antonin Raffin | Homepage
This is the second (and last) post on automatic hyperparameter optimization. In the first part, I introduced the challenges and main components of hyperparameter tuning (samplers, pruners, objective f...
araffin.github.io
April 28, 2025 at 9:50 AM
New blog post: Automatic Hyperparameter Tuning in Practice.
Part I was about the challenges and main components of hyperparameter tuning (samplers, …). Part II is about the practical application of this technique to RL with the @optuna.bsky.social and SB3 libraries.
araffin.github.io/post/optuna/
Part I was about the challenges and main components of hyperparameter tuning (samplers, …). Part II is about the practical application of this technique to RL with the @optuna.bsky.social and SB3 libraries.
araffin.github.io/post/optuna/
I'm diving into some serious hyperparameter tuning for my #chess program! ♟️
I'm using Optuna, and seriously, what a great API—it's super easy to use and makes the process so much cleaner. Getting closer to finding that optimal configuration!
#buildInPublic #chess
I'm using Optuna, and seriously, what a great API—it's super easy to use and makes the process so much cleaner. Getting closer to finding that optimal configuration!
#buildInPublic #chess
October 19, 2025 at 4:20 AM
I'm diving into some serious hyperparameter tuning for my #chess program! ♟️
I'm using Optuna, and seriously, what a great API—it's super easy to use and makes the process so much cleaner. Getting closer to finding that optimal configuration!
#buildInPublic #chess
I'm using Optuna, and seriously, what a great API—it's super easy to use and makes the process so much cleaner. Getting closer to finding that optimal configuration!
#buildInPublic #chess
ハイパーパラメータ探索。真面目にOptunaなどを使って探索用コード書くより、とりあえずパラメータを変えてジョブを投げておく方が時間効率が良いという状況。あんまり良くない。
June 20, 2025 at 12:04 PM
ハイパーパラメータ探索。真面目にOptunaなどを使って探索用コード書くより、とりあえずパラメータを変えてジョブを投げておく方が時間効率が良いという状況。あんまり良くない。