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nabenabe0928/README.md

Shuhei Watanabe: Bayesian Optimization & Robot Learning Researcher / Optuna Core Dev

Robotics Senior Research Scientist at SB Intuitions Corp. Core Optuna developer specializing in Bayesian optimization, with 1,500+ citations on Google Scholar.

Website - GH Pages

Shuhei's GitHub stats

🎓 Recent Publications

➡️ All publications...

📫 Email: shuhei.watanabe.utokyo@gmail.com

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  1. tpe tpe Public

    The tree-structured Parzen estimator (TPE) implementation and the simple running code for it

    Python 78 4

  2. constrained-tpe constrained-tpe Public

    [IJCAI'23] c-TPE: Tree-structured Parzen Estimator with Inequality Constraints for Expensive Hyperparameter Optimization

    Jupyter Notebook 8 2

  3. meta-learn-tpe meta-learn-tpe Public

    [IJCAI'23] Speeding Up Multi-Objective Hyperparameter Optimization by Task Similarity-Based Meta-Learning for the Tree-Structured Parzen Estimator

    Jupyter Notebook 10 3

  4. ped-anova ped-anova Public

    [IJCAI'23] PED-ANOVA: Efficiently Quantifying Hyperparameter Importance in Arbitrary Subspaces

    Python 9

  5. mfhpo-simulator mfhpo-simulator Public

    [Python3] The simulator for multi-fidelity or parallel optimization using tabular or surrogate benchmarks

    Python 7

  6. empirical-attainment-func empirical-attainment-func Public

    [Python3] The visualization for multi-objective optimization based on empirical attainment function.

    Python 9 1