Please let us know about related work that we should be inclusive of.  Alex Kulesza and Ben Taskar. Determinantal Point Processes for Machine Learning. [pdf]
 Mukund Narasimhan and Jeff A. Bilmes: PACLearning Bounded Treewidth Graphical Models. [pdf]
 Hoifung Poon and Pedro Domingos. SumProduct Networks: A New Deep Architecture. [pdf]
 Robert Gens and Pedro Domingos. Discriminative Learning of SumProduct Networks. [pdf]
 Daniel Tarlow, Inmar Givoni, and Richard Zemel. HOPMAP: Efficient Message Passing with High Order Potentials. [pdf]
 Dan Suciu, Dan Olteanu, Christopher Ré and Christoph Koch. Probabilistic Databases. [book]
 Stephen H. Bach, Bert Huang, Ben London, Lise Getoor. Hingeloss Markov Random Fields: Convex Inference for Structured Prediction. [pdf]
 Pedro Domingos and W. Austin Webb. A Tractable FirstOrder Probabilistic Logic. [pdf]
 Mathias Niepert. Markov Chains on Orbits of Permutation Groups. [pdf]
 Daniel Lowd and Pedro Domingos. Learning Arithmetic Circuits. [pdf]
 Daniel Lowd and Amirmohammad Rooshenas. Learning Markov Networks With Arithmetic Circuits [pdf]
 Vibhav Gogate and Pedro Domingos. Probabilistic Theorem Proving. [pdf]
 Tony Jebara. Perfect Graphs and Graphical Modeling. [pdf]
 K. S. Sesh Kumar and Francis Bach. Convex Relaxations for Learning Bounded Treewidth Decomposable Graphs. [pdf]
 Yariv Dror Mizrahi‚ Misha Denil and Nando de Freitas. Efficient Learning of Practical Markov Random Fields with Exact Inference. [pdf]
 G. Papandreou and A. Yuille, PerturbandMAP Random Fields: Using Discrete Optimization to Learn and Sample from Energy Models. [pdf]
 Martin J. Wainwright. Maximizing the “Wrong” Markov Random Field: Beneﬁts in the ComputationLimited Setting. [pdf]
