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Gaussian-Process Probability of Improvement from Maximum of Sample Path Sampler

This sampler searches for each trial based on Probability of Improvement from Maximum of Sample Path using Gaussian process.

Class or Function Names

  • PIMSSampler

Installation

$ pip install -r https://hub.optuna.org/samplers/gp_pims/requirements.txt

Example

Please see example.py.

Others

Reference

Shion Takeno, Yu Inatsu, Masayuki Karasuyama, Ichiro Takeuchi, Posterior Sampling-Based Bayesian Optimization with Tighter Bayesian Regret Bounds, Proceedings of the 41st International Conference on Machine Learning, PMLR 235:47510-47534, 2024.

Bibtex


@InProceedings{pmlr-v235-takeno24a,
  title = 	 {Posterior Sampling-Based {B}ayesian Optimization with Tighter {B}ayesian Regret Bounds},
  author =       {Takeno, Shion and Inatsu, Yu and Karasuyama, Masayuki and Takeuchi, Ichiro},
  booktitle = 	 {Proceedings of the 41st International Conference on Machine Learning},
  pages = 	 {47510--47534},
  year = 	 {2024},
  editor = 	 {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix},
  volume = 	 {235},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {21--27 Jul},
  publisher =    {PMLR},
  pdf = 	 {https://raw.githubusercontent.com/mlresearch/v235/main/assets/takeno24a/takeno24a.pdf},
  url = 	 {https://proceedings.mlr.press/v235/takeno24a.html}
}
Package
samplers/gp_pims
Author
Shion TAKENO
License
MIT License
Verified Optuna version
  • 3.6.1
Last update
2024-08-27