The following papers have been accepted for oral presentations at the workshop.

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• Exploiting Logical Structure in Lifted Probabilistic Inference
  Vibhav Gogate, Pedro Domingos


• Online Max-Margin Weight Learning with Markov Logic Networks
  Tuyen Huynh, Ray Mooney


• Stochastic Planning and Lifted Inference
  Roni Khardon


• Using Structural Motifs for Learning Markov Logic Networks
  Stanley Kok, Pedro Domingos


• Integrating Structured Metadata with Relational Affinity Propagation
  Anon Plangprasopchok, Kristina Lerman, Lise Getoor


• Bayesian Abductive Logic Programs
  Sindhu Vijaya Raghavan, Ray Mooney

The following papers have been accepted as posters at the workshop.

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• Automatic Inference in BLOG
  Nimar Arora,Stuart Russell, Erik Sudderth


• Relational Learning for Collective Classification of Entities in Images
  Anton Chechetka, Denver Dash, matthai Philipose


• Lifted Inference for Relational Continuous Models
  Jaesik Choi, David Hill, Eyal Amir


• Lifted Message Passing for Satisfiability
  Fabian Hadiji, Kristian Kersting, Babak Ahmadi


• Leveraging Ontologies for Lifted Probabilistic Inference and Learning
  Chloe Kiddon, Pedro Domingos


• Deep Transfer as Structure Learning in Markov Logic Networks
  David Moore, Andrea Danyluk


• Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models
  Sriraam Natarajan, Tushar Khot, Daniel Lowd, Prasad Tadepalli, Kristian Kersting, Jude Shavlik


• Efficient Lifting for Online Probabilistic Inference
  Aniruddh Nath, Pedro Domingos


• Approximate Lifted Belief Propagation
  Parag Singla, Aniruddh Nath, Pedro Domingos


• Machine Reading: A ``Killer App" for Statistical Relational AI
  Hoifung Poon, Pedro Domingos


• Declarative Probabilistic Programming for Undirected Graphical Models: Open Up to Scale Up
  Sebastian Riedel


• An Architectural Approach to Statistical Relational AI
  Paul Rosenbloom


• Probabilistic Programming for Planning Problems
  Ingo Thon, Bernd Gutmann, Guy Van den Broeck