Probabilistic Graphical Models

My Ph.D. work at MIT with Alan Willsky studied Gaussian graphical models with small feedback vertex sets. Three threads: message-passing inference, through feedback message passing (Liu et al., 2010; Liu et al., 2012) and its fully distributed, recursive form (Liu & Willsky, 2013); asymptotically exact sampling via stationary and non-stationary subgraph perturbations (Liu et al., 2013; Liu et al., 2015); and structure learning when the feedback nodes are observed or latent (Liu & Willsky, 2013).

References

2015

IEEE TSP

Sampling from Gaussian Markov Random Fields Using Stationary and Non-Stationary Subgraph Perturbations

Ying Liu, Oliver Kosut, and Alan S. Willsky

IEEE Transactions on Signal Processing, 2015

vol. 63, no. 3, pp. 576–589

DOI Bib HTML PDF

@article{liu2015sampling,
  note = {vol. 63, no. 3, pp. 576--589},
  title = {Sampling from {G}aussian {M}arkov Random Fields Using Stationary and Non-Stationary Subgraph Perturbations},
  author = {Liu, Ying and Kosut, Oliver and Willsky, Alan S.},
  journal = {IEEE Transactions on Signal Processing},
  volume = {63},
  number = {3},
  pages = {576--589},
  year = {2015},
  doi = {10.1109/TSP.2014.2375134},
}

2013

ISIT

Recursive FMP for Distributed Inference in Gaussian Graphical Models

Ying Liu and Alan S. Willsky

In IEEE International Symposium on Information Theory (ISIT), 2013

DOI Bib PDF

@inproceedings{liu2013recursive,
  title = {Recursive {FMP} for Distributed Inference in {G}aussian Graphical Models},
  author = {Liu, Ying and Willsky, Alan S.},
  booktitle = {IEEE International Symposium on Information Theory (ISIT)},
  year = {2013},
  doi = {10.1109/ISIT.2013.6620673},
}

ISIT

Sampling from Gaussian Graphical Models Using Subgraph Perturbations

Ying Liu, Oliver Kosut, and Alan S. Willsky

In IEEE International Symposium on Information Theory (ISIT), 2013

DOI Bib HTML PDF

@inproceedings{liu2013subgraph,
  title = {Sampling from {G}aussian Graphical Models Using Subgraph Perturbations},
  author = {Liu, Ying and Kosut, Oliver and Willsky, Alan S.},
  booktitle = {IEEE International Symposium on Information Theory (ISIT)},
  year = {2013},
  doi = {10.1109/ISIT.2013.6620676},
}

NeurIPS

Learning Gaussian Graphical Models with Observed or Latent FVSs

Ying Liu and Alan S. Willsky

In Advances in Neural Information Processing Systems (NeurIPS), 2013

arXiv Bib HTML PDF

@inproceedings{liu2013learning,
  title = {Learning {G}aussian Graphical Models with Observed or Latent {FVS}s},
  author = {Liu, Ying and Willsky, Alan S.},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  volume = {26},
  year = {2013},
}

2012

IEEE TSP

Feedback Message Passing for Inference in Gaussian Graphical Models

Ying Liu, Venkat Chandrasekaran, Animashree Anandkumar, and Alan S. Willsky

IEEE Transactions on Signal Processing, 2012

vol. 60, no. 8, pp. 4135–4150

arXiv Bib HTML PDF

@article{liu2012feedback,
  note = {vol. 60, no. 8, pp. 4135--4150},
  title = {Feedback Message Passing for Inference in {G}aussian Graphical Models},
  author = {Liu, Ying and Chandrasekaran, Venkat and Anandkumar, Animashree and Willsky, Alan S.},
  journal = {IEEE Transactions on Signal Processing},
  volume = {60},
  number = {8},
  pages = {4135--4150},
  year = {2012},
}

2010

ISIT

Feedback Message Passing for Inference in Gaussian Graphical Models

Ying Liu, Venkat Chandrasekaran, Animashree Anandkumar, and Alan S. Willsky

In IEEE International Symposium on Information Theory (ISIT), 2010

pp. 1683–1687

DOI Bib PDF

@inproceedings{liu2010feedback,
  note = {pp. 1683--1687},
  title = {Feedback Message Passing for Inference in {G}aussian Graphical Models},
  author = {Liu, Ying and Chandrasekaran, Venkat and Anandkumar, Animashree and Willsky, Alan S.},
  booktitle = {IEEE International Symposium on Information Theory (ISIT)},
  pages = {1683--1687},
  year = {2010},
  doi = {10.1109/ISIT.2010.5513321},
}