Uncertain and Probabilistic Reasoning
Uncertain reasoning in AI concerns various forms of "probabilistic" as opposed to logical inference. Our work in the area has focused on theory refinement for knowledge bases using uncertainty such as certainty-factor rule bases and Bayesian networks. Rather than building such knowledge-bases completely manually or learning them form scratch, our work focuses on using data to revise imperfect expert-supplied initial KB's.
People
Shruti Bhosale Formerly affiliated Masters Student shruti [at] cs utexas edu
Publications
     [Expand to show all 21][Minimize]
Natural Language Semantics Using Probabilistic Logic 2016
I. Beltagy, PhD Thesis, Department of Computer Science, The University of Texas at Austin.
Natural Language Semantics using Probabilistic Logic 2014
I. Beltagy, PhD proposal, Department of Computer Science, The University of Texas at Austin.
Montague Meets Markov: Deep Semantics with Probabilistic Logical Form 2013
I. Beltagy, Cuong Chau, Gemma Boleda, Dan Garrette, Katrin Erk, Raymond Mooney, Proceedings of the Second Joint Conference on Lexical and Computational Semantics (*Sem-2013) (2013), pp. 11--21.
University of Texas at Austin KBP 2013 Slot Filling System: Bayesian Logic Programs for Textual Inference 2013
Yinon Bentor, Amelia Harrison, Shruti Bhosale, and Raymond Mooney, In Proceedings of the Sixth Text Analysis Conference (TAC 2013) 2013.
Bayesian Logic Programs for Plan Recognition and Machine Reading 2012
Sindhu Raghavan, PhD Thesis, Department of Computer Science, University of Texas at Austin. 170.
Learning to "Read Between the Lines" using Bayesian Logic Programs 2012
Sindhu Raghavan, Raymond J. Mooney, and Hyeonseo Ku, Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (ACL-2012) (2012), pp. 349--358.
Extending Bayesian Logic Programs for Plan Recognition and Machine Reading 2011
Sindhu V. Raghavan, Technical Report, PhD proposal, Department of Computer Science, The University of Texas at Austin.
Improving the Accuracy and Scalability of Discriminative Learning Methods for Markov Logic Networks 2011
Tuyen N. Huynh, PhD Thesis, Department of Computer Science, University of Texas at Austin.
159 pages.
Discriminative Learning with Markov Logic Networks 2009
Tuyen N. Huynh, unpublished. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.
Max-Margin Weight Learning for Markov Logic Networks 2009
Tuyen N. Huynh and Raymond J. Mooney, In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Part 1, pp. 564--579, Bled, Slovenia, September 2009.
Discriminative Structure and Parameter Learning for Markov Logic Networks 2008
Tuyen N. Huynh and Raymond J. Mooney, In Proceedings of the 25th International Conference on Machine Learning (ICML), Helsinki, Finland, July 2008.
Bottom-Up Learning of Markov Logic Network Structure 2007
Lilyana Mihalkova and Raymond J. Mooney, In Proceedings of 24th International Conference on Machine Learning (ICML-2007), Corvallis, OR, June 2007.
Theory Refinement for Bayesian Networks with Hidden Variables 1998
Sowmya Ramachandran and Raymond J. Mooney, In Proceedings of the Fifteenth International Conference on Machine Learning (ICML-98), pp. 454--462, Madison, WI, July 1998.
Theory Refinement of Bayesian Networks with Hidden Variables 1998
Sowmya Ramachandran and Raymond J. Mooney, PhD Thesis, Department of Computer Sciences, University of Texas at Austin. 139 pages. Also appears as Technical Report AI 98-265, Artificial Intelligence Lab, University of Texas at Austin.
Combining Symbolic and Connectionist Learning Methods to Refine Certainty-Factor Rule-Bases 1996
J. Jeffrey Mahoney, PhD Thesis, Department of Computer Sciences, University of Texas at Austin. 113 pages.
Revising Bayesian Network Parameters Using Backpropagation 1996
Sowmya Ramachandran and Raymond J. Mooney, In Proceedings of the International Conference on Neural Networks (ICNN-96), Special Session on Knowledge-Based Artificial Neural Networks, pp. 82--87, Washington DC, June 1996.
Refinement of Bayesian Networks by Combining Connectionist and Symbolic Techniques 1995
Sowmya Ramachandran, Unpublished Ph.D. Thesis Proposal.
Comparing Methods For Refining Certainty Factor Rule-Bases 1994
J. Jeffrey Mahoney and Raymond J. Mooney, In Proceedings of the Eleventh International Workshop on Machine Learning (ML-94), pp. 173--180, Rutgers, NJ, July 1994.
Modifying Network Architectures For Certainty-Factor Rule-Base Revision 1994
J. Jeffrey Mahoney and Raymond J. Mooney, In Proceedings of the International Symposium on Integrating Knowledge and Neural Heuristics (ISIKNH-94), pp. 75--85, Pensacola, FL, May 1994.
Combining Connectionist and Symbolic Learning to Refine Certainty-Factor Rule-Bases 1993
J. Jeffrey Mahoney and Raymond J. Mooney, Connection Science (1993), pp. 339-364.
Combining Symbolic and Neural Learning to Revise Probabilistic Theories 1992
J. Jeffrey Mahoney and Raymond J. Mooney, In Proceedings of the ML92 Workshop on Integrated Learning in Real Domains, Aberdeen, Scotland, July 1992.
Labs
Machine Learning