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Tuyen N. Huynh, unpublished.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. |