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ORCID4th StarAI@AAAI 2014: Québec City, QC, Canada
- Statistical Relational Artificial Intelligence, Papers from the 2014 AAAI Workshop, Québec City, Québec, Canada, July 27, 2014. AAAI Technical Report WS-14-13, AAAI 2014
- Sriraam Natarajan:
Organizers. - Guy Van den Broeck, Kristian Kersting, Sriraam Natarajan, David Poole:
Preface. - Udi Apsel, Kristian Kersting, Martin Mladenov:
Lifting Relational MAP-LPs using Cluster Signatures. - Islam Beltagy, Raymond J. Mooney:
Efficient Markov Logic Inference for Natural Language Semantics. - Ismail Ilkan Ceylan, Rafael Peñaloza:
Reasoning in the Description Logic BEL Using Bayesian Networks. - Jaesik Choi, Eyal Amir, Tianfang Xu, Albert J. Valocchi:
Parameter Estimation for Relational Kalman Filtering. - Matthew C. Dirks, Andrew Csinger, Andrew Bamber, David Poole:
Representation, Reasoning, and Learning for a Relational Influence Diagram Applied to a Real-Time Geological Domain. - Golnoosh Farnadi, Stephen H. Bach, Marie-Francine Moens, Lise Getoor, Martine De Cock:
Extending PSL with Fuzzy Quantifiers. - Eric Gribkoff, Guy Van den Broeck, Dan Suciu:
Understanding the Complexity of Lifted Inference and Asymmetric Weighted Model Counting. - Seyed Mehran Kazemi, David Buchman, Kristian Kersting, Sriraam Natarajan, David Poole:
Relational Logistic Regression: The Directed Analog of Markov Logic Networks. - Tushar Khot, Sriraam Natarajan, Jude W. Shavlik:
Classification from One Class of Examples for Relational Domains. - Junkyu Lee, Radu Marinescu, Rina Dechter:
Applying Marginal MAP Search to Probabilistic Conformant Planning: Initial Results. - Daniel Lowd, Brenton Lessley, Mino De Raj:
Towards Adversarial Reasoning in Statistical Relational Domains. - Aniruddh Nath, Pedro M. Domingos:
Automated Debugging with Tractable Probabilistic Programming. - Aniruddh Nath, Pedro M. Domingos:
Learning Tractable Statistical Relational Models. - Mathias Niepert, Pedro M. Domingos:
Tractable Probabilistic Knowledge Bases: Wikipedia and Beyond. - Masaaki Nishino, Akihiro Yamamoto, Masaaki Nagata:
A Sparse Parameter Learning Method for Probabilistic Logic Programs. - Shrutika Poyrekar, Sriraam Natarajan, Kristian Kersting:
A Deeper Empirical Analysis of CBP Algorithm: Grounding Is the Bottleneck. - Joris Renkens, Angelika Kimmig, Guy Van den Broeck, Luc De Raedt:
Explanation-Based Approximate Weighted Model Counting for Probabilistic Logics. - Fatemeh Riahi, Oliver Schulte, Qing Li:
A Proposal for Statistical Outlier Detection in Relational Structures. - Sebastian Riedel, Sameer Singh, Vivek Srikumar, Tim Rocktäschel, Larysa Visengeriyeva, Jan Noessner:
WOLFE: Strength Reduction and Approximate Programming for Probabilistic Programming. - Brian E. Ruttenberg, Matthew P. Wilkins, Avi Pfeffer:
Hierarchical Reasoning with Probabilistic Programming. - Zhengya Sun, Zhuoyu Wei, Jue Wang, Hongwei Hao:
Scalable Learning for Structure in Markov Logic Networks. - Deepak Venugopal, Vibhav Gogate
:
Evidence-Based Clustering for Scalable Inference in Markov Logic. - Mihaela Verman, Philip Stutz, Abraham Bernstein:
Solving Distributed Constraint Optimization Problems Using Ranks. - Jonas Vlasselaer, Wannes Meert, Guy Van den Broeck, Luc De Raedt:
Efficient Probabilistic Inference for Dynamic Relational Models. - William Yang Wang, Kathryn Mazaitis, William W. Cohen:
ProPPR: Efficient First-Order Probabilistic Logic Programming for Structure Discovery, Parameter Learning, and Scalable Inference.
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