Bayesian Coreset Algorithms
- Dominant language
- Python
- Stars
- 43
- Forks
- 6
- Avg merge
- 3d 22h
- Merged PRs (30d)
- 10
Description
### What's the new feature?
Add Bayesian Coreset Algorithms (unsupervised & supervised):
- [Automated Scalable Bayesian Inference via Hilbert Coresets](https://www.jmlr.org/papers/v20/17-613.html)
- [Sparse Variational Inference: Bayesian Coresets from Scratch](https://proceedings.neurips.cc/paper_files/paper/2019/file/7bec7e63a493e2d61891b1e4051ef75a-Paper.pdf)
Currently no Bayesian functionality in Coreax, do we want it?
### What value does this add?
Add Bayesian functionality to Coreax.
### Is there an alternative you've considered?
_No response_
### Additional context
_No response_
Contributor guide
Research direction
No files, tests, or entry points are named in the issue. Start by reading the two linked Bayesian coreset papers and reviewing Coreax's existing coreset algorithm structure; done would require an agreed scope and support for both unsupervised and supervised Bayesian coreset algorithms.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100