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