Difference between revisions of "Graphical Models Reading Group"
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== Potential Papers == | == Potential Papers == | ||
D. Weiss, B. Sapp, and B. Taskar. "[http://arxiv.org/pdf/1208.3279.pdf Structured Prediction Cascades]." arXiv, August 2012. | D. Weiss, B. Sapp, and B. Taskar. "[http://arxiv.org/pdf/1208.3279.pdf Structured Prediction Cascades]." arXiv, August 2012. | ||
| + | |||
| + | Noorshams, Nima, and Martin J. Wainwright. "[http://arxiv.org/pdf/1212.3850v1 Belief propagation for continuous state spaces: Stochastic message-passing with quantitative guarantees]." arXiv preprint arXiv:1212.3850 (2012). | ||
| + | |||
| + | G. Andrew and J. Bilmes. "[http://melodi.ee.washington.edu/~bilmes/mypubs/andrew2012-archipelagos.pdf Memory-efficient inference in dynamic graphical models using multiple cores]." AISTATS 2012. | ||
== Good Resources == | == 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. "[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. "Graphical models, exponential families, and variational inference." Foundations and Trends® in Machine Learning 1.1-2 (2008): 1-305. "[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. | ||
Revision as of 04:32, 25 April 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.
Meeting Schedule
First meeting to take place between May1-3.
If you are interested in coming, please fill out the following doodle poll so that we can decide on a meeting time and room: http://doodle.com/7u5ztt639683z5p6
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.
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. "Graphical Models, Exponential Families, and Variational Inference." Foundations and Trends® in Machine Learning 1.1-2 (2008): 1-305.
