jeek
ICML18: JEEK - Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models
Science Score: 20.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
○codemeta.json file
-
○.zenodo.json file
-
○DOI references
-
✓Academic publication links
Links to: arxiv.org -
✓Committers with academic emails
1 of 3 committers (33.3%) from academic institutions -
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (12.1%) to scientific vocabulary
Repository
ICML18: JEEK - Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models
Basic Info
Statistics
- Stars: 1
- Watchers: 4
- Forks: 0
- Open Issues: 1
- Releases: 0
Metadata Files
README.md
JEEK
Tool JEEK: A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models
Paper: @Arxiv | updated version at Here
accepted at 2018 ICML
R package: URL
GitRepo for R package: URL
R
install.packages("jeek")
library(jeek)
demo(jeek)
Abstract
We consider the problem of including additional knowledge in estimating sparse Gaussian graphical models (sGGMs) from aggregated samples, arising often in bioinformatics and neuroimaging applications. Previous joint sGGM estimators either fail to use existing knowledge or cannot scale-up to many tasks (large $K$) under a high-dimensional (large $p$) situation. In this paper, we propose a novel \underline{J}oint \underline{E}lementary \underline{E}stimator incorporating additional \underline{K}nowledge (JEEK) to infer multiple related sparse Gaussian Graphical models from large-scale heterogeneous data. Using domain knowledge as weights, we design a novel hybrid norm as the minimization objective to enforce the superposition of two weighted sparsity constraints, one on the shared interactions and the other on the task-specific structural patterns. This enables JEEK to elegantly consider various forms of existing knowledge based on the domain at hand and avoid the need to design knowledge-specific optimization. JEEK is solved through a fast and entry-wise parallelizable solution that largely improves the computational efficiency of the state-of-the-art $O(p^5K^4)$ to $O(p^2K^4)$. We conduct a rigorous statistical analysis showing that JEEK achieves the same convergence rate $O(\log(Kp)/n_{tot})$ as the state-of-the-art estimators that are much harder to compute. Empirically, on multiple synthetic datasets and two real-world data, JEEK outperforms the speed of the state-of-arts significantly while achieving the same level of prediction accuracy.
Citations
latex
@conference{wang2018jeek,
Author = {Wang, Beilun and Sekhon, Arshdeep and Qi, Yanjun},
Booktitle = {Proceedings of The 35th International Conference on Machine Learning (ICML)},
Title = {A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models},
Year = {2018}}
}
Support or Contact
Having trouble with our tools? Please contact Beilun and we’ll help you sort it out.
Owner
- Name: QData
- Login: QData
- Kind: organization
- Email: yanjun@virginia.edu
- Location: https://qdata.github.io/qdata-page/
- Website: https://qdata.github.io/qdata-page
- Twitter: Qdatalab
- Repositories: 48
- Profile: https://github.com/QData
http://www.cs.virginia.edu/yanjun/
GitHub Events
Total
Last Year
Committers
Last synced: over 3 years ago
All Time
- Total Commits: 16
- Total Committers: 3
- Avg Commits per committer: 5.333
- Development Distribution Score (DDS): 0.313
Top Committers
| Name | Commits | |
|---|---|---|
| Yanjun Qi | q****2@g****m | 11 |
| MIkeWang | m****g@g****m | 3 |
| blw921 | b****w@v****u | 2 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 12 months ago
All Time
- Total issues: 1
- Total pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Total issue authors: 1
- Total pull request authors: 0
- Average comments per issue: 0.0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- amssljc (1)
Pull Request Authors
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- cran 203 last-month
- Total dependent packages: 0
- Total dependent repositories: 1
- Total versions: 3
- Total maintainers: 1
cran.r-project.org: jeek
A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models
- Homepage: https://github.com/QData/jeek
- Documentation: http://cran.r-project.org/web/packages/jeek/jeek.pdf
- License: GPL-2
-
Latest release: 1.1.1
published about 8 years ago
Rankings
Maintainers (1)
Dependencies
- R >= 3.0.0 depends
- igraph * depends
- lpSolve * depends
- pcaPP * depends
- parallel * suggests
- R >= 3.0.0 depends
- igraph * depends
- lpSolve * depends
- pcaPP * depends
- parallel * suggests
- R >= 3.0.0 depends
- igraph * depends
- lpSolve * depends
- pcaPP * depends
- parallel * suggests