@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.}
}