Related Publications

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: PAC-Learning Bounded Tree-width Graphical Models. [pdf]
  • Hoifung Poon and Pedro Domingos. Sum-Product Networks: A New Deep Architecture. [pdf]
  • Robert Gens and Pedro Domingos. Discriminative Learning of Sum-Product Networks. [pdf]
  • Daniel Tarlow, Inmar Givoni, and Richard Zemel. HOP-MAP: 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. Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction. [pdf]
  • Pedro Domingos and W. Austin Webb. A Tractable First-Order 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, Perturb-and-MAP 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 Computation-Limited Setting. [pdf]