JuliaGaussianProcesses / JuliaGaussianProcesses/AbstractGPs.jl
Hyperparameter optimization & maintanance
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- Dominant language
- Julia
- Stars
- 287
- Forks
- 27
- PR merge metrics
- No merged PRs in 30d
Description
Hi!
Thank you for this wonderful package. I am implementing a Trust Region Bayesian Optimization (TuRBO) algorithm during Google Summer of Code, and would like to depend on AbstractGPs. A crucial part in this algorithm is recurrent optimization of hyperparameters, that appropriately stretch the trust regions.
I saw in the docs an example, Mauna Loa, for optimizing hyperparameters and wanted to ask about the current plans for providing these functionalities directly from the package. I think that I wil need to implement it in some way in the future. Would you like me to help you out with the implementation here?
I thank you very much in advance!
Contributor guide
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with the Mauna Loa hyperparameter-optimization example mentioned in the documentation and review the current package plans for providing this functionality directly. Define what recurrent hyperparameter optimization should support for the TuRBO use case, then document the expected behavior and validation needed for a completed implementation.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100