JuliaGaussianProcesses / JuliaGaussianProcesses/AbstractGPs.jl
VFE/DTC's internal implementation needs a reference
Open
Nobody has claimed this yet.
good first issue
- Dominant language
- Julia
- Stars
- 287
- Forks
- 27
- PR merge metrics
- No merged PRs in 30d
Description
The internal implementation of VFE/DTC are non-standard and need references to their derivations.
Related literature:
- VFE: M. K. Titsias. "Variational learning of inducing variables in sparse Gaussian
processes". In: Proceedings of the Twelfth International Conference on Artificial
Intelligence and Statistics. 2009. - DTC: M. Seeger, C. K. I. Williams and N. D. Lawrence. "Fast Forward Selection to Speed Up
Sparse Gaussian Process Regression". In: Proceedings of the Ninth International Workshop on
Artificial Intelligence and Statistics. 2003
https://github.com/JuliaGaussianProcesses/AbstractGPs.jl/pull/308#issuecomment-1084420431
CC: @willtebbutt
Contributor guide
No contributing guide indexed for this repository
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 by locating the internal VFE and DTC implementations and reviewing the discussion in pull request #308. Compare each implementation with the derivations in the cited Titsias and Seeger, Williams, and Lawrence papers; done means the relevant derivation references are added.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- machine-learning
- Issue type
- Documentation
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100