https://github.com/qdata/diffee
AISTAT2018: Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure
https://github.com/qdata/diffee
Last synced: about 2 months ago
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AISTAT2018: Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure
- Host: GitHub
- URL: https://github.com/qdata/diffee
- Owner: QData
- License: apache-2.0
- Created: 2018-02-27T18:04:47.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2019-08-28T16:29:38.000Z (over 5 years ago)
- Last Synced: 2025-01-11T11:26:37.927Z (4 months ago)
- Language: R
- Homepage: https://cran.r-project.org/web/packages/diffee/index.html
- Size: 26.8 MB
- Stars: 0
- Watchers: 5
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# DIFFEE
## R package "diffee": @ [CRAN Website](https://cran.r-project.org/web/packages/diffee/index.html)
```R
install.packages("diffee")
library(diffee)
demo(diffeeDemo)
```
## Reference
```latex
@InProceedings{pmlr-v84-wang18f,
title = {Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure},
author = {Beilun Wang and arshdeep Sekhon and Yanjun Qi},
booktitle = {Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics},
pages = {1691--1700},
year = {2018},
editor = {Amos Storkey and Fernando Perez-Cruz},
volume = {84},
series = {Proceedings of Machine Learning Research},
address = {Playa Blanca, Lanzarote, Canary Islands},
month = {09--11 Apr},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v84/wang18f/wang18f.pdf},
url = {http://proceedings.mlr.press/v84/wang18f.html},
abstract = {We focus on the problem of estimating the change in the dependency structures of two $p$-dimensional Gaussian Graphical models (GGMs). Previous studies for sparse change estimation in GGMs involve expensive and difficult non-smooth optimization. We propose a novel method, DIFFEE for estimating DIFFerential networks via an Elementary Estimator under a high-dimensional situation. DIFFEE is solved through a faster and closed form solution that enables it to work in large-scale settings. We conduct a rigorous statistical analysis showing that surprisingly DIFFEE achieves the same asymptotic convergence rates as the state-of-the-art estimators that are much more difficult to compute. Our experimental results on multiple synthetic datasets and one real-world data about brain connectivity show strong performance improvements over baselines, as well as significant computational benefits.}
}
```
## more details in project [website](http://jointggm.org/)