Graphical Models Reading Group
This is the wiki page for topics to be discussed in the Graphical Models Reading Group, starting Spring 2013.
We will hold informal meetings to discuss papers regarding Probabilistic Graphical Models(PGMs). The field is very expansive, and as such paper topics may include exact/approximate inference techniques, variational methods, structure/parameter learning, interesting applications, and so on. Each weekly meeting will have a discussion leader who will both propose the paper to be discussed and get the discussion ball rolling for the meeting.
For questions, or other, please contact either:
- John Halloran - halloj3 [at] ee.wash....edu
- Scott Wisdom - swisdom [at] ee.wash....edu
Contents
Announcements
Our second meeting has been announced. Doodle poll to decide meeting time and subsequent quarter meeting times: http://doodle.com/5gmyes226bzy9nxp
Meeting Schedule
Next meeting will be sometime the week of March 6-10, exact date and time TBD.
Next meeting reading details
Next meeting leads: Dj and Rishabh.
We'll be discussing the extended version of the Po-Ling Loh and Martin J Wainwright paper "Structure estimation for discrete graphical models: Generalized covariance matrices and their inverses." This week, we will mostly be focusing on the proofs of the main theorems of the paper.
Email list
You can subscribe here: https://mailman.cs.washington.edu/mailman/listinfo/graphicalmodels-rg
Prior Meetings
| Date | Paper | Authors | Venue | Leader | Info |
|---|---|---|---|---|---|
| 2013-05-03 | "Structure estimation for discrete graphical models: Generalized covariance matrices and their inverses" | Po-Ling Loh and Martin J Wainwright | NIPS 2012 | John | Discussed consequences of main theorems |
Potential Papers
D. Weiss, B. Sapp, and B. Taskar. "Structured Prediction Cascades." arXiv, August 2012.
N. Noorshams and M. J. Wainwright. "Belief propagation for continuous state spaces: Stochastic message-passing with quantitative guarantees." arXiv preprint arXiv:1212.3850 (2012).
G. Andrew and J. Bilmes. "Memory-efficient inference in dynamic graphical models using multiple cores." AISTATS 2012.
C. Sutton and A. McCallum. "An Introduction to Conditional Random Fields." arXiv preprint arXiv:1011.4088 (2010).
A. Banerjee and S. Merugu and I. S. Dhillon and J. Ghosh. "Clustering with Bregman Divergences." The Journal of Machine Learning Research 6 (2005): 1705-1749.
J. Friedman and T. Hastie and R. Tibshirani. "Sparse inverse covariance estimation with the graphical lasso." Biostatistics 9.3 (2008): 432-441.
K. Mohan and M. J. Chung and S. Han and D. Witten and S. Lee and M. Fazel. "Structured Learning of Gaussian Graphical Models." Advances in Neural Information Processing Systems(NIPS) 25. 2012.
Good Resources
Wainwright, Martin J., and Michael I. Jordan. "Graphical Models, Exponential Families, and Variational Inference." Foundations and Trends® in Machine Learning 1.1-2 (2008): 1-305.
Jeff Bilmes' course on Dynamic Grpahical Models' website: "EE596A - Dynamic Graphical Models - Winter Quarter, 2013"
