uGLAD

Sparse graph recovery by optimizing deep unrolled networks. This work proposes uGLAD (Sparse graph recovery by optimizing deep unrolled networks. arxiv) which is a unsupervised version of a previous GLAD model (GLAD: Learning Sparse Graph Recovery (ICLR 2020 - link).

Talk

https://www.youtube.com/watch?v=Mx9VSQJACsA

Key benefits & features:

• Solution to Graphical Lasso: A better alternative to solve the Graphical Lasso problem as
  • The neural networks of the uGLAD enable adaptive choices of the hyperparameters which leads to better performance than the existing algorithms
  • No need to pre-specify the sparsity related regularization hyperparameters
  • Requires less number of iterations to converge due to neural network based acceleration of the unrolled optimization algorithm (Alternating Minimization)
  • GPU based acceleration can be leveraged
  • Novel consensus strategy which robustly handles missing values by leveraging the multi-task learning ability of the model
  • Multi-task learning mode that solves the graphical lasso objective to recover multiple graphs with a single uGLAD model
• Glasso loss function: The loss is the logdet objective of the graphical lasso 1/M(-1*log|theta|+ <S, theta>), where M=num_samples, S=input covariance matrix, theta=predicted precision matrix.
• Ease of usability: Matches the I/O signature of sklearn GraphicalLassoCV, so easy to plug-in to the existing code.

uGLAD architecture: Unrolled deep model

Setup

The setup.sh file contains the complete procedure of creating a conda environment to run mGLAD model. run bash setup.sh
In case of dependencies conflict, one can alternatively use this command conda env create --name uGLAD --file=environment.yml.

demo-uGLAD notebook

A minimalist working example of uGLAD. It is a good entry point to understand the code structure as well as the uGLAD model.

Citation

If you find this method useful, kindly cite the following 2 associated papers:

• uGLAD: Sparse graph recovery by optimizing deep unrolled networks. arxiv
  @inproceedings{
  shrivastava2022a,
  title={A deep learning approach to recover conditional independence graphs},
  author={Harsh Shrivastava and Urszula Chajewska and Robin Abraham and Xinshi Chen},
  booktitle={NeurIPS 2022 Workshop: New Frontiers in Graph Learning},
  year={2022},
  url={https://openreview.net/forum?id=kEwzoI3Am4c}
  }

• GLAD:
  @article{shrivastava2019glad,
  title={GLAD: Learning sparse graph recovery},
  author={Shrivastava, Harsh and Chen, Xinshi and Chen, Binghong and Lan, Guanghui and Aluru, Srinvas and Liu, Han and Song, Le},
  journal={arXiv preprint arXiv:1906.00271},
  year={2019}
  }