Department of Computer Science

Machine Learning Research Group

University of Texas at Austin Artificial Intelligence Lab

Publications: 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.
  1. Natural Language Semantics using Probabilistic Logic
    [Details] [PDF] [Slides]
    Islam Beltagy
    October 2014. PhD proposal, Department of Computer Science, The University of Texas at Austin.
  2. University of Texas at Austin KBP 2013 Slot Filling System: Bayesian Logic Programs for Textual Inference
    [Details] [PDF]
    Yinon Bentor and Amelia Harrison and Shruti Bhosale and Raymond Mooney
    In Proceedings of the Sixth Text Analysis Conference (TAC 2013), 2013.
  3. Montague Meets Markov: Deep Semantics with Probabilistic Logical Form
    [Details] [PDF] [Slides]
    Islam Beltagy, Cuong Chau, Gemma Boleda, Dan Garrette, Katrin Erk, Raymond Mooney
    In Proceedings of the Second Joint Conference on Lexical and Computational Semantics (*Sem-2013), 11--21, Atlanta, GA, June 2013.
  4. Bayesian Logic Programs for Plan Recognition and Machine Reading
    [Details] [PDF] [Slides]
    Sindhu Raghavan
    PhD Thesis, Department of Computer Science, University of Texas at Austin, December 2012. 170.
  5. Learning to "Read Between the Lines" using Bayesian Logic Programs
    [Details] [PDF] [Slides]
    Sindhu Raghavan and Raymond J. Mooney and Hyeonseo Ku
    In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (ACL-2012), 349--358, July 2012.
  6. Extending Bayesian Logic Programs for Plan Recognition and Machine Reading
    [Details] [PDF] [Slides]
    Sindhu V. Raghavan
    Technical Report, PhD proposal, Department of Computer Science, The University of Texas at Austin, May 2011.
  7. Improving the Accuracy and Scalability of Discriminative Learning Methods for Markov Logic Networks
    [Details] [PDF] [Slides]
    Tuyen N. Huynh
    PhD Thesis, Department of Computer Science, University of Texas at Austin, May 2011.
    159 pages.
  8. Discriminative Learning with Markov Logic Networks
    [Details] [PDF] [Slides]
    Tuyen N. Huynh
    October 2009. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.
  9. Max-Margin Weight Learning for Markov Logic Networks
    [Details] [PDF] [Slides]
    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, 564--579, Bled, Slovenia, September 2009.
  10. Discriminative Structure and Parameter Learning for Markov Logic Networks
    [Details] [PDF] [Slides]
    Tuyen N. Huynh and Raymond J. Mooney
    In Proceedings of the 25th International Conference on Machine Learning (ICML), Helsinki, Finland, July 2008.
  11. Bottom-Up Learning of Markov Logic Network Structure
    [Details] [PDF]
    Lilyana Mihalkova and Raymond J. Mooney
    In Proceedings of 24th International Conference on Machine Learning (ICML-2007), Corvallis, OR, June 2007.
  12. Theory Refinement for Bayesian Networks with Hidden Variables
    [Details] [PDF]
    Sowmya Ramachandran and Raymond J. Mooney
    In Proceedings of the Fifteenth International Conference on Machine Learning (ICML-98), 454--462, Madison, WI, July 1998.
  13. Theory Refinement of Bayesian Networks with Hidden Variables
    [Details] [PDF]
    Sowmya Ramachandran and Raymond J. Mooney
    PhD Thesis, Department of Computer Sciences, University of Texas at Austin, Austin, TX, May 1998. 139 pages. Also appears as Technical Report AI 98-265, Artificial Intelligence Lab, University of Texas at Austin.
  14. Combining Symbolic and Connectionist Learning Methods to Refine Certainty-Factor Rule-Bases
    [Details] [PDF]
    J. Jeffrey Mahoney
    PhD Thesis, Department of Computer Sciences, University of Texas at Austin, May 1996. 113 pages.
  15. Revising Bayesian Network Parameters Using Backpropagation
    [Details] [PDF]
    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, 82--87, Washington DC, June 1996.
  16. Refinement of Bayesian Networks by Combining Connectionist and Symbolic Techniques
    [Details] [PDF]
    Sowmya Ramachandran
    1995. Unpublished Ph.D. Thesis Proposal.
  17. Comparing Methods For Refining Certainty Factor Rule-Bases
    [Details] [PDF]
    J. Jeffrey Mahoney and Raymond J. Mooney
    In Proceedings of the Eleventh International Workshop on Machine Learning (ML-94), 173--180, Rutgers, NJ, July 1994.
  18. Modifying Network Architectures For Certainty-Factor Rule-Base Revision
    [Details] [PDF]
    J. Jeffrey Mahoney and Raymond J. Mooney
    In Proceedings of the International Symposium on Integrating Knowledge and Neural Heuristics (ISIKNH-94), 75--85, Pensacola, FL, May 1994.
  19. Combining Connectionist and Symbolic Learning to Refine Certainty-Factor Rule-Bases
    [Details] [PDF]
    J. Jeffrey Mahoney and Raymond J. Mooney
    Connection Science:339-364, 1993.
  20. Combining Symbolic and Neural Learning to Revise Probabilistic Theories
    [Details] [PDF]
    J. Jeffrey Mahoney and Raymond J. Mooney
    In Proceedings of the ML92 Workshop on Integrated Learning in Real Domains, Aberdeen, Scotland, July 1992.