Methods › General › Optimization › Tree-structured Parzen Estimator Approach (TPE)
Tree-structured Parzen Estimator Approach (TPE)
Introduced by James Bergstra et al. in Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
The archive carries no description for this method.
Papers archive 2025-07-28
3 shown of 3, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Optimising Neural Fractional Differential Equations for Performance and Efficiency 20 Oct 2024 · 2 repositories
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MANGO: A Python Library for Parallel Hyperparameter Tuning 22 May 2020 · 1 repository · arXiv:2005.11394
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Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms 12 Dec 2013 · 2 repositories
Tasks archive 2025-07-28
8 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| BIG-bench Machine Learning | 2 |
| Bayesian Optimization | 2 |
| Hyperparameter Optimization | 2 |
| Distributed Computing | 1 |
| Distributed Optimization | 1 |
| Experimental Design | 1 |
| Model Selection | 1 |
| Scheduling | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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