uglad 2.0.0

A deep unrolled architecture to recover conditional independence graphs. A better alternative to BCD and other graphical lasso solvers.

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

Users

pip install uglad

Developers

The setup.sh file contains the complete procedure of creating a conda environment to run uGLAD model.

bash setup.sh

demo-uGLAD notebook

A minimalist working example of uGLAD is given in examples/demo-uGLAD.ipynb notebook. 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}
  }