Difference between revisions of "Graphical Models Reading Group"
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Wainwright, Martin J., and Michael I. Jordan. "[http://www.eecs.berkeley.edu/~wainwrig/Papers/WaiJor08_FTML.pdf Graphical Models, Exponential Families, and Variational Inference]." Foundations and Trends® in Machine Learning 1.1-2 (2008): 1-305. | Wainwright, Martin J., and Michael I. Jordan. "[http://www.eecs.berkeley.edu/~wainwrig/Papers/WaiJor08_FTML.pdf Graphical Models, Exponential Families, and Variational Inference]." Foundations and Trends® in Machine Learning 1.1-2 (2008): 1-305. | ||
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| + | Jeff Bilmes' course on Dynamic Grpahical Models' website: "[http://j.ee.washington.edu/~bilmes/classes/ee596a_winter_2013/ EE596A - Dynamic Graphical Models - Winter Quarter, 2013]" | ||
Revision as of 05:46, 1 May 2013
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
Wiki created and currently under heavy construction. Our first meeting quickly approaching.
Our first meeting has been scheduled: First meeting to take place May 3 at 12:30-1:30 in EEB M406.
Meeting Schedule
First meeting to take place May 3 at 12:30-1:30 in EEB M406.
First meeting reading details
First meeting lead: John Halloran.
We'll be discussing a recent paper by Po-Ling Loh and Martin J Wainwright titled "Structure estimation for discrete graphical models: Generalized covariance matrices and their inverses" from NIPS 2012, where they prove conditions under which the (generalized)information matrix between jointly distributed discrete random variables denotes the edges in the corresponding graphical model of the random variables.
The paper is available here or click on its title in the above paragraph.
Email list
You can subscribe here: https://mailman.cs.washington.edu/mailman/listinfo/graphicalmodels-rg
Prior Meetings
| Date | Paper | Authors | Venue | Leader | Info |
|---|
Potential Papers
D. Weiss, B. Sapp, and B. Taskar. "Structured Prediction Cascades." arXiv, August 2012.
Noorshams, Nima, and Martin 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).
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"
